How Do We Assess the Trustworthiness of AI? Introducing the Trustworthiness Assessment Model (TrAM)
Open accessAbstract
Designing trustworthy AI-based systems and enabling external parties to accurately assess the trustworthiness of these systems are crucial objectives. Only if trustors assess system trustworthiness accurately, they can base their trust on adequate expectations about the system and reasonably rely on or reject its outputs. However, the process by which trustors assess a system’s actual trustworthiness to arrive at their perceived trustworthiness remains underexplored. In this paper, we conceptually distinguish between actual and perceived trustworthiness, trust propensity, trust, and trusting behavior. Drawing on psychological models of how humans assess other people’s characteristics, we present the two-level Trustworthiness Assessment Model (TrAM). At the micro level, we propose that trustors assess system trustworthiness based on cues associated with the system. The accuracy of this assessment depends on cue relevance and availability on the system’s side, and on cue detection and utilization on the human’s side. At the macro level, we propose that individual micro-level trustworthiness assessments propagate across different trustors – one stakeholder’s trustworthiness assessment of a system affects other stakeholder’s trustworthiness assessments of the same system. The TrAM advances existing models of trust and sheds light on factors influencing the (accuracy of) trustworthiness assessments. It contributes to theoretical clarity in trust research, has implications for the measurement of trust-related variables, and practical implications for system design, stakeholder training, and regulation related to trustworthiness assessments.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces the Trustworthiness Assessment Model (TrAM), a conceptual two-level model distinguishing actual trustworthiness (AT), perceived trustworthiness (PT), trust propensity, trust, and trusting behavior. It proposes that accurate trustworthiness assessment depends on cue relevance/availability on the system side and cue detection/utilization on the trustor side, and that assessments propagate across stakeholders via secondary cues.
Key findings
- TrAM separates AT from PT and positions the trustworthiness assessment process between them; assessment is accurate when a trustor’s PT matches the system’s AT.
- At the micro level, accuracy depends on cue relevance and availability on the system side and cue detection and utilization on the trustor side; irrelevant cues such as anthropomorphism can distort PT.
- At the macro level, a trustworthiness propagation process occurs: stakeholders such as system designers, certification bodies, deployers, and end users produce secondary cues colored by their individual standards, which can influence other stakeholders’ assessments.
- Implications include designing systems to make relevant cues available, training trustors in cue detection/utilization, and considering propagation and the relative nature of trustworthiness in regulation and certification. No numerical results are reported.
Methods
- Conceptual/theoretical paper; no empirical data, sample, or statistical analysis are reported.
- Develops TrAM by drawing on psychological models—Funder’s Realistic Accuracy Model and Brunswik’s Lens Model—and translating them to human assessment of artificial systems.
- Main analysis is conceptual: defines model components and relations at micro and macro levels, using figures and a “requirement list” metaphor, and derives design, training, and regulatory implications.
Limitations
- Stated: the model assumes theoretical processes and concepts that are currently difficult to capture and may not be readily accessible to stakeholders; trustors may not communicate their individual standards or be aware of the cues they used.
- Stated: individual standards are claimed to differ across trustors and be task- and context-specific, which may make the concept appear of limited value due to variability; the authors expect overlap but also idiosyncratic combinations and weights.
- Evident from the text: the paper is conceptual and does not empirically validate the proposed model; its propositions remain to be tested.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 187 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Overtrust poses risks such as reduced vigilance and oversight.
“Overtrust poses potentially dramatic risks [52], such as reduced vigilance and oversight [47].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [52]
Not checked in the free run.
- cites [47]
Not checked in the free run.
- Premisecitation
Both overtrust and undertrust risks are minimized when trust is just right and trusting behavior is appropriate.
“Both risks are minimized when trust in a system is “just right” and trusting behavior is appropriate (e.g., when users accept accurate and reject inaccurate system output; [18,82,103]).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [18]
Not checked in the free run.
- cites [82]
Not checked in the free run.
- cites [103]
Not checked in the free run.
- Premisecitation
Undertrust carries risks such as users ignoring valid system information in favor of inferior manual assessments.
“Similarly, undertrust carries risks, such as users ignoring valid system information in favor of inferior manual assessments [52].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [52]
Not checked in the free run.
- Premisecitation
Systems that actually lack trustworthiness may still be perceived as trustworthy.
“Conversely, systems that actually lack trustworthiness may still be perceived as trustworthy [71,97,107].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [71]
Not checked in the free run.
- cites [97]
Not checked in the free run.
- cites [107]
Not checked in the free run.
- Premisecitation
Characteristics associated with actual trustworthiness, such as accuracy and fairness, influence people's perceptions of trustworthiness.
“Research has shown that characteristics associated with actual trustworthiness (e.g., accuracy, fairness [50]) influence people's perceptions of trustworthiness [125].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [50]
Not checked in the free run.
- cites [125]
Not checked in the free run.
- Premisecitation
A system being actually trustworthy is not sufficient to develop high levels of perceived trustworthiness.
“However, the fact that a system is actually trustworthy does not seem to be sufficient to develop high levels of perceived trustworthiness [27,31,88].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [27]
Not checked in the free run.
- cites [31]
Not checked in the free run.
- cites [88]
Not checked in the free run.
- Premisecitation
The trustworthiness assessment process is an important antecedent to achieving just-right trust and appropriate trusting behavior.
“The trustworthiness assessment process is an important antecedent to achieving “just right” trust and appropriate trusting behavior [5,91,142].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5]
Not checked in the free run.
- cites [91]
Not checked in the free run.
- cites [142]
Not checked in the free run.
- Premisecitation
Different people may arrive at different perceptions of trustworthiness depending on characteristics of the human-system interaction, the person assessing the system, and the situation.
“Relatedly, different people may arrive at different perceptions of trustworthiness depending on the characteristics of the human-system interaction [27], the person assessing the system [98], and the situation [88].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [27]
Not checked in the free run.
- cites [98]
Not checked in the free run.
- cites [88]
Not checked in the free run.
- Interpretationreasoning
The accuracy of micro-level trustworthiness assessment depends on cue relevance and availability on the system's side and on cue detection and utilization on the human's side.
“The accuracy of this assessment depends on cue relevance and availability on the system's side, and on cue detection and utilization on the human's side.”
Quote in the paperNot a substantial claim - Premisenone given
Propensity to trust plays a major role in differences in the trust development process between trustors who experience the same system.
“Propensity to trust plays a major role in differences in the trust development process between trustors who experience the same system.”
Quote in the paperNot a substantial claim - Premisecitation
Even when system characteristics remain the same, different people may end up with different perceptions of the system's trustworthiness.
“Although the characteristics of the system remain the same, each person may end up with a different perception of the system's trustworthiness [64,107].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [64]
Not checked in the free run.
- cites [107]
Not checked in the free run.
- Interpretationnone given
At the micro level, trustors assess system trustworthiness based on cues associated with the system.
“At the micro level, we propose that trustors assess system trustworthiness based on cues associated with the system.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Different stakeholders influence each other's trustworthiness assessments.
“The model highlights that different stakeholders influence each other's trustworthiness assessments.”
Quote in the paperSubstantial claim - Premisecitation
Even if the degree of trust is high, if perceived risks are also high, trustors may decide to perform a task themselves.
“For example, even if the degree of trust is high, if the perceived risks are also high, trustors may decide to perform a task themselves [17,147,149].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [17]
Not checked in the free run.
- cites [147]
Not checked in the free run.
- cites [149]
Not checked in the free run.
- Premisecitation
Trustworthiness consists of multiple facets.
“Regardless of whether trustworthiness is considered an attribute of the trustee or a subjective perception of attributes of a trustee, research suggests that trustworthiness consists of multiple facets [5,28,50,54,82,91].”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [5]
Not checked in the free run.
- cites [28]
Not checked in the free run.
- cites [50]
Not checked in the free run.
- cites [54]
Not checked in the free run.
- cites [82]
Not checked in the free run.
- cites [91]
Not checked in the free run.
- Premisecitation
Trust is influenced by perceived trustworthiness.
“Trust is influenced by PT [5,22,91,101].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [5]
Not checked in the free run.
- cites [22]
Not checked in the free run.
- cites [91]
Not checked in the free run.
- cites [101]
Not checked in the free run.
- Premisecitation
People seem to underestimate the trustworthiness of systems after seeing the system fail (algorithm aversion).
“algorithm aversion (i.e., that people seem to underestimate the trustworthiness of systems after seeing the system fail; [27])”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27]
Not checked in the free run.
- Premisecitation
The trustworthiness assessment process is prone to inaccuracies, for instance, assessment of seemingly objective system characteristics such as accuracy varies between different observers.
“This is especially true because research has shown that the trustworthiness assessment process is prone to inaccuracies, for instance, that even the assessment of seemingly objective system characteristics such as their accuracy varies between different observers [32,89,107,116].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [32]
Not checked in the free run.
- cites [89]
Not checked in the free run.
- cites [107]
Not checked in the free run.
- cites [116]
Not checked in the free run.
- Interpretationreasoning
Understanding the trustworthiness assessment process and the factors that affect its accuracy is crucial to understanding whether downstream trust and trusting behavior are adequate.
“Therefore, understanding the trustworthiness assessment process (i.e., the transition between AT and PT), as well as understanding the factors that affect the accuracy of the trustworthiness assessment process, is crucial to understanding whether downstream trust and trusting behavior are adequate.”
Quote in the paperNot a substantial claim - Interpretationnone given
Whether trust and trusting behavior are adequate depends heavily on the characteristics of the trustee and their relation to the goals of the trustor.
“We propose that whether trust and trusting behavior are adequate depends heavily on the characteristics of the trustee and their relation to the goals of the trustor.”
Quote in the paperNot a substantial claim - Premisecitation
People initially tend to overestimate the trustworthiness of systems for certain tasks (automation bias).
“automation bias phenomenon (i.e., that people initially tend to overestimate the trustworthiness of systems for certain tasks; [98,108])”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [98]
Not checked in the free run.
- cites [108]
Not checked in the free run.
- Premisecitation
Behavior may appear to reflect trusting behavior but may actually reflect situational factors such as time pressure or social conformity.
“In line with this, sometimes behavior may appear to reflect trusting behavior, but may actually reflect the consequences of situational factors, such as time pressure [115] or social conformity [3].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [115]
Not checked in the free run.
- cites [3]
Not checked in the free run.
- Premisecitation
We assess latent constructs in interactions with people.
“We also assess latent constructs in interactions with people [40].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [40]
Not checked in the free run.
- Premisecitation
Most or even all facets of actual trustworthiness consist of subfacets.
“Most or even all of these facets of AT will consist of subfacets [52,82].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [52]
Not checked in the free run.
- cites [82]
Not checked in the free run.
- Premisecitation
In assessing latent constructs in interactions, we interpret available cues to assess a person's actual characteristics, resulting in perceived characteristics.
“We interpret available cues (e.g., a person's behavior and their clothing) to assess a person's actual characteristics (e.g., actual extraversion) resulting in our perceived characteristics (e.g., perceived extraversion) of that person.”
Quote in the paperSubstantial claim - Premisecitation
Regardless of its accuracy, an assessment and corresponding expectations can influence the assessor's subsequent assessments and behavior toward the target.
“Regardless of its accuracy, the assessment and the corresponding expectations can influence the assessor's subsequent assessments and the assessor's behavior toward the target [117].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [117]
Not checked in the free run.
- Premisecitation
Cognitive abilities and personality are latent constructs that cannot be directly observed or measured to obtain their true value.
“Both are latent constructs, as neither can be directly observed or measured to obtain their “true value” (i.e., their ground truth) [14].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14]
Not checked in the free run.
- Interpretationreasoning
The degree to which a trustor's perceived trustworthiness matches a system's actual trustworthiness depends on the availability and relevance of cues on the system's side and on the detection and utilization of cues on the human's side.
“Consequently, the degree to which a trustor's PT matches a system's AT depends on the availability and relevance of cues on the system's side and on the detection and utilization of cues on the human's side.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Cues available to trustors are, to some extent, relevant to the assessment of a system's actual trustworthiness.
“These cues are, to some extent, relevant to the assessment of a system's AT.”
Quote in the paperSubstantial claim - Premisecitation
The accuracy of assessing another person's characteristics varies depending on the characteristics of the target, the cues available, and the observer.
“This assessment varies in its accuracy depending on (a) the characteristics of the target, (b) the cues available for assessing the target's characteristics, and (c) the observer assessing the target's characteristics [40].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [40]
Not checked in the free run.
- Premisecitation
A mismatch between a target's actual personality and an observer's perceived personality can lead to challenges in target-observer interactions.
“Indeed, a mismatch between a target's actual personality and an observer's perceived personality of the target can lead to challenges in targetobserver interactions [40,53].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [40]
Not checked in the free run.
- cites [53]
Not checked in the free run.
- Interpretationreasoning
Because system accuracy is not directly measurable, people use manifest variables, i.e., observable cues, to assess system accuracy.
“Because system accuracy itself is also not directly measurable, people use manifest variables, i.e., observable cues, to assess system accuracy.”
Quote in the paperSubstantial claim - Interpretationcitation
Individual standards for system trustworthiness are influenced by trustors' goals and interests, culture, ethically and socially motivated values, and the normative and regulatory framework.
“Individual standards are influenced by trustors' goals and interests, the culture in which trustors grew up (e.g., collectivistic vs. individualistic cultures [4]), ethically and socially motivated values (e.g., cooperation vs. competition) [130], as well as the normative and regulatory framework in which trustors operate (e.g., under the influence of the European General Data Protection”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [4]
Not checked in the free run.
- cites [130]
Not checked in the free run.
- Interpretationreasoning
The specification of facets of trustworthiness is likely to differ between different groups or individuals.
“However, the specification of these facets of trustworthiness is likely to differ between different (groups of) individuals.”
Quote in the paperNot a substantial claim - Interpretationcitation
Individual standards could be clustered by the facets assumed to constitute trustworthiness, such as ability, benevolence, and integrity.
“Thus, individual standards could be clustered by the facets that are assumed to constitute trustworthiness (e.g., ability, benevolence, integrity; [91]).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [91]
Not checked in the free run.
- Interpretationcitation
Changes in system characteristics can result from improvements to the system, such as optimizing technical functionalities including algorithmic fairness, robustness, explainability, and safety.
“Changes in the system characteristics can result from improvements to the system, for instance, by optimizing its technical functionalities”
Quote in the paperNot a substantial claim0 of 7 sources checked- cites [13]
Not checked in the free run.
- cites [84]
Not checked in the free run.
- cites [127]
Not checked in the free run.
- cites [21]
Not checked in the free run.
- cites [146]
Not checked in the free run.
- cites [114]
Not checked in the free run.
- cites [128]
Not checked in the free run.
- Premisecitation
Perceived trustworthiness is a latent construct that can only be measured indirectly, as is common in research assessing people's perceptions of system trustworthiness.
“PT is another latent construct that that can only be measured indirectly, as is common in research assessing people's perceptions of system trustworthiness (e.g., by asking people to report on their perceived trustworthiness of a system [1,58,73,90,116], or by observing people's interactions with systems [135,149]).”
Quote in the paperNot a substantial claim0 of 7 sources checked- cites [1]
Not checked in the free run.
- cites [58]
Not checked in the free run.
- cites [73]
Not checked in the free run.
- cites [90]
Not checked in the free run.
- cites [116]
Not checked in the free run.
- cites [135]
Not checked in the free run.
- cites [149]
Not checked in the free run.
- Interpretationreasoning
A trustor's perceived trustworthiness is determined in part by how they actively and passively select, interpret, and weigh cues.
“Trustor's PT is determined in part by how they actively and passively select, interpret, and weigh cues.”
Quote in the paperSubstantial claim - Interpretationreasoning
Changes in system characteristics may also be due to changes in the environment.
“Changes in the system characteristics may also be due to changes in the environment.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Trustors are constantly, consciously or unconsciously, searching for, encountering, and interpreting cues to assess the actual trustworthiness of a system.
“Thus, trustors are constantly (consciously or unconsciously) searching for, encountering, and interpreting cues to assess the AT of a system.”
Quote in the paperSubstantial claim - Interpretationreasoning
Cues are the interface between actual trustworthiness and perceived trustworthiness.
“Cues are the interface between AT and PT.”
Quote in the paperSubstantial claim - Premisecitation
Changes in individual standards may be due to new experiences or knowledge gained by the trustor.
“Changes in the individual standards may be due to new experiences or knowledge gained by the trustor [36,52,130].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [36]
Not checked in the free run.
- cites [52]
Not checked in the free run.
- cites [130]
Not checked in the free run.
- Premisecitation
Lay people's PT of a system appears to be more strongly influenced by a small sample of personal experiences with a system than by information from large-scale testing.
“Furthermore, lay people's PT of a system appears to be more strongly influenced by a small sample of personal experiences with a system than by information from large-scale testing [26,112,147].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [26]
Not checked in the free run.
- cites [112]
Not checked in the free run.
- cites [147]
Not checked in the free run.
- Premisecitation
Cues can be used to assess system trustworthiness.
“A variety of cues can be used to assess system trustworthiness.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [124]
Not checked in the free run.
- cites [18]
Not checked in the free run.
- cites [85]
Not checked in the free run.
- cites [136]
Not checked in the free run.
- cites [142]
Not checked in the free run.
- Premisecitation
A trustor's individual standards could top-down guide their attention to and detection of trustworthiness cues.
“In addition, a trustor's individual standards could top-down guide their attention to and detection of trustworthiness cues [41,56].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [41]
Not checked in the free run.
- cites [56]
Not checked in the free run.
- Premisecitation
The accuracy of trustworthiness assessment depends on the relevance and availability of cues on the system side and on the detection and utilization of cues on the trustor side.
“According to Funder [40], this accuracy depends on the relevance and availability of cues on the side of the system and on the detection and utilization of cues on the side of the trustor.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Funder [40]
Not checked in the free run.
- Premisecitation
Research has provided evidence for inaccuracies of trustor's PT due to suboptimal cue utilization.
“Research has provided evidence for inaccuracies of trustor's PT due to suboptimal cue utilization.”
Quote in the paperNot a substantial claim - Interpretationreasoning
On the system side, cue relevance and availability determine how accurately AT can be assessed from the cues.
“On the system side, cue relevance and availability determine how accurately AT can be assessed from the cues.”
Quote in the paperSubstantial claim - Interpretationreasoning
Throughout the trustworthiness propagation process, different stakeholders assess a system's AT to arrive at their PT.
“Throughout the trustworthiness propagation process, different stakeholders assess a system's AT to arrive at their PT.”
Quote in the paperSubstantial claim - Premisecitation
Seeing others interact with or talk about a system can strongly influence trustworthiness assessments.
“For example, seeing others interact with or talk about a system, can strongly influence trustworthiness assessments.”
Quote in the paperNot a substantial claim - Premisecitation
Cue utilization may be influenced by cognitive biases or implicit attitudes toward automation or AI.
“Cue utilization may be influenced by cognitive biases or implicit attitudes toward automation or AI [76,96,97].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [76]
Not checked in the free run.
- cites [96]
Not checked in the free run.
- cites [97]
Not checked in the free run.
- Interpretationcitation
If no cues are available or detected, it is difficult to accurately assess system trustworthiness.
“If no cues are available or detected, it is difficult to accurately assess system trustworthiness.”
Quote in the paperSubstantial claim - Interpretationcitation
Improper utilization of relevant, available, and detected cues will result in a low accuracy of the trustworthiness assessment.
“Finally, improper utilization of relevant, available, and detected cues will result in a low accuracy of the trustworthiness assessment.”
Quote in the paperSubstantial claim - Premisecitation
The domain, task, and system knowledge of the human trustor might influence cue utilization.
“Finally, the domain, task, and system knowledge of the human trustor might influence cue utilization [149].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [149]
Not checked in the free run.
- Premisecitation
The first-error effect suggests that system errors are weighted more heavily when they occur at the beginning of a human-system interaction than when they occur later.
“For instance, the first-error effect [7] suggests that system errors (as a cue for the absence of system trustworthiness) are weighted more heavily when they occur at the beginning of a human-system interaction than when they occur later in the interaction.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7]
Not checked in the free run.
- Interpretationcitation
If only irrelevant cues are available, this will prevent an accurate assessment of AT.
“If only irrelevant cues are available, this will prevent an accurate assessment of AT.”
Quote in the paperSubstantial claim - Interpretationreasoning
Trustworthiness cues arise not only from the system itself, but also from the stakeholders who assess the AT of a system with respect to their individual standards.
“This implies that trustworthiness cues arise not only from the system itself, but also from the stakeholders who assess the AT of a system with respect to their individual standards.”
Quote in the paperSubstantial claim - Interpretationreasoning
The same system could be considered as not trustworthy enough by a certification institution in the EU and trustworthy in, say, the US.
“Note that this means that the same system could be considered as not trustworthy enough by a certification institution in the EU and trustworthy in, say, the US.”
Quote in the paperNot a substantial claim - Premisecitation
In seminal models of trust in automation and organizational trust, there formerly existed a gap because these models only included the PT of the trustor without describing how trustors arrive at their PT given the trustees' AT.
“In seminal models of trust in automation (i.e., [82]) and organizational trust (i.e., [91]) there formerly existed a gap because these models only included the PT of the trustor without describing how trustors arrive at their PT given the trustees' AT.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [82]
Not checked in the free run.
- cites [91]
Not checked in the free run.
- Interpretationcitation
Secondary cues can streamline the assessment by indicating that another trustor has already completed the trustworthiness assessment.
“Secondary cues can streamline the assessment by indicating that another trustor has already completed the trustworthiness assessment.”
Quote in the paperNot a substantial claim - Premisecitation
Beyond efficiency, secondary cues can also strongly influence the accuracy of trustworthiness assessments.
“Beyond efficiency, secondary cues can also strongly influence the accuracy of trustworthiness assessments [44,118].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [44]
Not checked in the free run.
- cites [118]
Not checked in the free run.
- Premisecitation
The stakes of the situation influence the decision of whether people will actually show trusting behavior.
“At this point, the stakes of the situation (i.e., the possible positive as well as the possible negative outcomes) will influence the decision of whether people will actually show trusting behavior [91].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [91]
Not checked in the free run.
- Interpretationreasoning
Perceived presence and absence of trustworthiness are not just simple additions of cues, but rather a function of cues.
“At this point, we would like to emphasize that the perceived presence and absence of trustworthiness are not just simple additions of cues, but rather a function of cues.”
Quote in the paperSubstantial claim - Interpretationreasoning
If stakeholders strongly utilize one cue, such as a trustworthiness label, to form their perceived trustworthiness, this may lead to other secondary cues that are strongly dependent on this one cue.
“If these stakeholders strongly utilize this one cue, say a trustworthiness label, to form their perceived trustworthiness, this may also lead to other secondary cues that are strongly dependent on this one cue.”
Quote in the paperNot a substantial claim - Interpretationreasoning
There may be a conceptual gap if we assume that the assessment of AT directly translates into trust.
“As we have argued in this paper, there may be a conceptual gap if we assume that the assessment of AT directly translates into trust.”
Quote in the paperNot a substantial claim - Interpretationnone given
During the trustworthiness propagation process, certain stakeholders may produce highly influential secondary cues that can strongly affect the trustworthiness assessment of many subsequent stakeholders.
“Moreover, during the trustworthiness propagation process, certain stakeholders may produce highly influential secondary cues that can strongly affect the trustworthiness assessment of many subsequent stakeholders.”
Quote in the paperSubstantial claim - Premisecitation
Secondary cues may be designed with a specific goal in mind, such as convincing others of a system's trustworthiness or of embedded moral values.
“They may be designed with a specific goal in mind [33,70], such as convincing others of a system's trustworthiness or of embedded moral values [18,137].”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [33]
Not checked in the free run.
- cites [70]
Not checked in the free run.
- cites [18]
Not checked in the free run.
- cites [137]
Not checked in the free run.
- Interpretationreasoning
A trustor's perceived trustworthiness may be justified given the available cues even if it does not accurately reflect the system's actual trustworthiness.
“In other words, a trustor's PT may be justified given the available cues, even if it does not accurately reflect the AT.”
Quote in the paperSubstantial claim - Premisecitation
Calibrated trust has often been measured by observing people's trusting behavior toward a system.
“With respect to measurement of calibrated trust, rather than trying to capture trustworthiness or trust, most commonly, calibrated trust has often been measured by observing people's trusting behavior toward a system (e.g., rejecting vs. accepting system outputs [104,149]).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [104]
Not checked in the free run.
- cites [149]
Not checked in the free run.
- Interpretationreasoning
If another trustor is confronted with a large number of secondary cues and assumes they all tell a unique story about the actual trustworthiness of a system, this can lead to an inaccurate perceived trustworthiness.
“If another trustor is confronted with this large number of secondary cues, assuming that they all tell a unique story about the AT of a system, this can lead to an inaccurate PT.”
Quote in the paperSubstantial claim - Interpretationreasoning
This can result in a large number of secondary cues that are all dependent on and thus highly correlated with a single influential cue.
“This can result in a large number of secondary cues that are all dependent on (and are thus highly correlated with) a single influential cue.”
Quote in the paperSubstantial claim - Interpretationreasoning
The model allows for a case where all cues available to the trustor about the system have been adequately detected and utilized, and yet this perfectly justified perceived trustworthiness may not accurately reflect the system's actual trustworthiness.
“Our model allows for a case where all the cues available to the trustor about the system have been adequately detected and utilized, and yet this perfectly justified PT may not accurately reflect the AT of the system.”
Quote in the paperSubstantial claim - Premisecitation
Misinterpretations of cues intended to communicate specific characteristics of different products have been shown for ecolabels, fair trade labels, and trusted shops labels.
“Such possible misinterpretations of cues intended to communicate specific characteristics of different products were also shown for”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [24]
Not checked in the free run.
- cites [121]
Not checked in the free run.
- Interpretationcitation
Trust in a system is often the consequence of trust in other stakeholders.
“This implies that trust in a system is often the consequence of trust in other stakeholders (see also [33,66,70]).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [33]
Not checked in the free run.
- cites [66]
Not checked in the free run.
- cites [70]
Not checked in the free run.
- Interpretationreasoning
Examining cue utilization provides information about a trustor's individual standards, expectations of the system, and knowledge and weighting of cue relevance.
“Examining cue utilization provides information about trustor's individual standards, expectations of the system, and trustor's knowledge and weighting of cue relevance.”
Quote in the paperNot a substantial claim - Interpretationnone given
At the macro level, the trustworthiness propagation process proposes that the assessment of a system's actual trustworthiness is a net of micro-level assessments.
“At the macro level, the trustworthiness propagation process proposes that the assessment of a system's AT is a net of micro-level assessments.”
Quote in the paperSubstantial claim - Interpretationreasoning
For an accurate assessment, downstream trustors need to assess not only the actual trustworthiness of a system, but also the actual trustworthiness of providers of secondary cues.
“As a consequence, for an accurate assessment, downstream trustors need to assess not only the AT of a system, but also the AT of providers of secondary cues.”
Quote in the paperSubstantial claim - Interpretationcitation
The assessment of system trustworthiness involves the combination of the assessment of trust in other people, trust in institutions, and trust in systems.
“Thus, the assessment of system trustworthiness involves the combination of the assessment of trust in other people, trust in institutions, and trust in systems [80].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [80]
Not checked in the free run.
- Interpretationcitation
High-risk application contexts of systems where legislation calls for human oversight may be cases where accurate trustworthiness assessments are critical.
“Such cases may be high-risk application contexts of systems where legislation around the world calls for human oversight [43,59].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [43]
Not checked in the free run.
- cites [59]
Not checked in the free run.
- Interpretationreasoning
For black-box models such as deep neural networks, it may be especially challenging to achieve accurate trustworthiness assessments because these systems may make it more difficult to provide relevant cues.
“However, for such black-box models (e.g., using deep neural networks), it may be especially challenging to achieve accurate trustworthiness assessments because these systems may make it more difficult to provide relevant cues.”
Quote in the paperSubstantial claim - Interpretationreasoning
Effective human oversight requires that humans are able to adequately assess when to rely on system output and when to reject it.
“Effective human oversight requires that humans are able to adequately assess when to rely on system output and when to reject it.”
Quote in the paperNot a substantial claim - Interpretationcitation
We cannot deploy systems in high-risk contexts where it is impossible to make relevant cues available.
“This may also imply that we cannot deploy systems in high-risk contexts where it is impossible to make relevant cues available [43].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [43]
Not checked in the free run.
- May combine two claims. 61
- Classified as probably not a substantial claim. 100
May combine two claims.
InterpretationcitationAccurate trustworthiness assessment enables trustors to base their trust on adequate expectations about the system and to make informed decisions about trusting behavior.
“When someone's trustworthiness assessment is accurate, they can base their trust on adequate expectations about the system's capabilities and limitations [82] and make informed decisions about their trusting behavior.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [82]
Not checked in the free run.
May combine two claims.
InterpretationreasoningInaccurate trustworthiness assessment can lead to trust based on false expectations and inadequate trust, with risks of overtrust and undertrust.
“If the assessment is inaccurate, the person's trust in the system may be based on false expectations, and consequently, may be inadequate.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPropensity to trust develops across the lifespan and may be particularly important in unfamiliar situations with unknown trustees.
“Propensity to trust develops across the lifespan and may be particular important in unfamiliar situations with unknown trustees [5].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5]
Not checked in the free run.
May combine two claims.
PremisecitationPropensity to trust is a relatively stable human disposition, which limits the ability of external factors to influence trust development processes through propensity to trust.
“Research suggests that propensity to trust is a relatively stable human disposition [5], which limits the ability of external factors to influence trust development processes through propensity to trust.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5]
Not checked in the free run.
May combine two claims.
PremisecitationAlthough the disposition to trust humans cannot be directly extrapolated to machines, the literature on trust in automation recognizes propensity to trust as a stable disposition.
“While the disposition to trust humans cannot be directly extrapolated to the disposition to trust machines, the literature on trust in automation also recognizes the notion of propensity to trust as a stable disposition [52,57,82].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [52]
Not checked in the free run.
- cites [57]
Not checked in the free run.
- cites [82]
Not checked in the free run.
May combine two claims.
PremisecitationPerceptions of trustworthiness are situational and subjective.
“Perceptions of trustworthiness are situational and subjective [5,52,91].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5]
Not checked in the free run.
- cites [52]
Not checked in the free run.
- cites [91]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenAt the macro level, individual micro-level trustworthiness assessments propagate across different trustors, such that one stakeholder's assessment affects other stakeholders' assessments of the same system.
“At the macro level, we propose that individual micro-level trustworthiness assessments propagate across different trustors -one stakeholder's trustworthiness assessment of a system affects other stakeholder's trustworthiness assessments of the same system.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationTrustworthiness perceptions reflect a trustor's assessment of the trustee's abilities, principles, and intentions, as well as beliefs about whether the trustee could help the trustor achieve their goals.
“In this understanding, trustworthiness perceptions reflect a trustor's assessment of the trustee's abilities, principles, and intentions, as well as a trustor's beliefs about whether the trustee could help the trustor to achieve their goals [5,22,81,82,91].”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [5]
Not checked in the free run.
- cites [22]
Not checked in the free run.
- cites [81]
Not checked in the free run.
- cites [82]
Not checked in the free run.
- cites [91]
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningTo understand how people arrive at their perceived trustworthiness of a system, it is necessary to understand both individual assessments of system trustworthiness at the micro level and trustworthiness propagation processes among trustors at the societal level.
“In conclusion, we propose that in order to understand how people arrive at their perceived trustworthiness of a system, it is necessary to better understand both individual assessments of system trustworthiness (i.e., the micro level) and trustworthiness propagation processes among trustors at the societal level (i.e., the macro level).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenIndividual standards of a few stakeholders can propagate to the trustworthiness assessments of all other stakeholders along the system lifecycle.
“because the individual standards of a few stakeholders can propagate to the trustworthiness assessments of all other stakeholders along the system lifecycle.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningAn accurate assessment of a system's actual trustworthiness is the basis for adequate trust and adequate trusting behavior.
“Second, we propose that an accurate assessment of a system's actual trustworthiness is the basis for adequate trust, and adequate trusting behavior.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationIn interpersonal trust, ability, benevolence, and integrity subsume all facets of perceived trustworthiness.
“Mayer et al. [91] propose that ability (a “group of skills, competencies, and characteristics that enable a party to have influence within some specific domain”, p. 717), benevolence (“the extent to which a trustee is believed to want to do good to the trustor, aside from an egocentric profit motive”, p. 718), and integrity (“the trustor's perception that the trustee adheres to a set of principles”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [91]
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationLee and See translated ability, benevolence, and integrity to trust in automation as performance, purpose, and process.
“Lee and See [82] translated these facets to trust in automation referring to performance (“the competency or expertise as demonstrated by its ability to achieve the operator's goals”, p. 59), purpose (“corresponds to faith and benevolence and reflects the perception that the trustee has a positive orientation towards the trustor”, p. 59) and process (“the degree to which the automation's algorithm”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [82]
Not checked in the free run.
May combine two claims.
PremisecitationAlthough trust and trusting behavior can be positively related, the stakes associated with the actual decision to engage in trusting behavior prevent trust from translating directly into trusting behavior.
“Although trust and trusting behavior can be positively related [73], the stakes associated with the actual decision to engage in trusting behavior toward a trustee prevent trust from translating directly into trusting behavior.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [73]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationIf perceptions of trustworthiness and intentions of trust influence behavior, then trustworthiness, trust, and trusting behavior are expected to be positively related.
“Nevertheless, if we assume that perceptions (i.e., of trustworthiness) and intentions (i.e., trust) influence behavior, then trustworthiness, trust, and trusting behavior are expected to be positively related [135].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [135]
Not checked in the free run.
- The authors state this as an assumption: Perceptions of trustworthiness and intentions of trust influence behavior.
May combine two claims.
PremisecitationInaccurate trustworthiness assessments are a cause of miscalibrated trust, as evident in automation bias and algorithm aversion.
“Moreover, inaccurate trustworthiness assessments as a cause of miscalibrated trust [75,88,109] are evident in research on the automation bias phenomenon (i.e., that people initially tend to overestimate the trustworthiness of systems for certain tasks; [98,108]) and in research on algorithm aversion (i.e., that people seem to underestimate the trustworthiness of systems after seeing the system”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [75]
Not checked in the free run.
- cites [88]
Not checked in the free run.
- cites [109]
Not checked in the free run.
May combine two claims.
PremisecitationPrevious research has distinguished between actual and perceived trustworthiness and has emphasized the importance of accurately assessing actual trustworthiness.
“We acknowledge that previous research has also distinguished between AT and PT, and has also emphasized the importance of accurately assessing AT [82,103,138].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [82]
Not checked in the free run.
- cites [103]
Not checked in the free run.
- cites [138]
Not checked in the free run.
May combine two claims.
InterpretationcitationIn the TrAM, automation bias and algorithm aversion boil down to the system's actual trustworthiness being either lower (automation bias) or higher (algorithm aversion) than the trustor's perceived trustworthiness.
“In the TrAM, these examples boil down to the fact that the system's AT was either lower (automation bias) or higher (algorithm aversion) than the trustor's PT of that system [64,71].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [64]
Not checked in the free run.
- cites [71]
Not checked in the free run.
Classified as probably not a substantial claim.
PremisereasoningCharacteristics of other people that are to be assessed are often latent constructs.
“Characteristics of other people that are to be assessed are often latent constructs.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThere is typically no way to perfectly measure individual facets of actual trustworthiness, nor to accurately assess how they combine to actual trustworthiness.
“However, there is typically no way to perfectly measure these individual facets (e.g., because only a subset of all available data can be accessed to assess system performance), nor to accurately assess how they combine to AT [52,82].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [52]
Not checked in the free run.
- cites [82]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
InterpretationreasoningHuman trustors need to detect and utilize certain, possibly many, cues to arrive at their perceived trustworthiness.
“Human trustors then need to detect and utilize certain (and possibly many) cues to arrive at their PT.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningThe relationship between actual trustworthiness and perceived trustworthiness sets the stage for everything that follows in the trust development process, including adequate trust and trusting behavior.
“We propose that this relationship between AT and PT sets the stage for everything that follows in the trust development process (e.g., in terms of adequate trust and trusting behavior).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationBenevolence, integrity, and ability are treated as facets of actual trustworthiness.
“For example, benevolence, integrity, and ability are treated as facets of AT [50,82,91].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [50]
Not checked in the free run.
- cites [82]
Not checked in the free run.
- cites [91]
Not checked in the free run.
May combine two claims.
InterpretationreasoningIn the micro-level trustworthiness assessment process, trustors cannot directly assess a system's actual trustworthiness; instead, they assess it on the basis of available cues.
“Specifically, in the micro-level trustworthiness assessment process (Figure 2), we propose that trustors cannot directly assess a system's AT.Instead, they assess a system's AT on the basis of available cues.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningA system's actual trustworthiness depends on the system characteristics and on the individual standards of trustworthiness.
“To summarize, a system's AT depends on the system characteristics and on the individual standards of trustworthiness.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenSystem characteristics in the context-free space are objective in the sense that they do not depend on the trustor.
“It is important to distinguish that system characteristics in this (only theoretically existing) context-free space are also objective in the sense that they do not depend on the trustor.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe specification of a system's ability depends on users' goals, interests, and personal alternatives to the system.
“For instance, the specification of a system's ability depends on the users' goals (e.g., screening vs. decision making), interests (e.g., saving money vs. data privacy), and their personal alternatives to the system (e.g., one's own ability to perform the task at hand).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningSystem accuracy is a facet of a system's ability.
“For instance, system accuracy will be a facet of a system's ability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenActual trustworthiness is always subjective in the sense that it is relative to the trustor's individual standards of trustworthiness.
“In contrast, AT is always subjective in the sense that it is relative to the trustor's individual standards of trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningDifferences in individual standards may result from the granularity and specification of the trustor's standards.
“Differences in the individual standards may also result from the granularity (from general to detailed) and specification (from explicit to implicit) of the trustor's standards.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIndividual standards are not arbitrary; they refer to a common understanding of the concept of trustworthiness of systems, influenced by public discussions about trustworthy AI.
“Individual standards are not arbitrary: they refer to a common understanding of the concept of trustworthiness of systems, which is also influenced by public discussions about trustworthy AI [29,50,151].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [29]
Not checked in the free run.
- cites [50]
Not checked in the free run.
- cites [151]
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningActual trustworthiness reflects the degree to which the system characteristics match a trustor's individual standards for trustworthiness with respect to a particular task and point in time.
“Specifically, AT reflects the degree to which the system characteristics match a trustor's individual standards for trustworthiness with respect to a particular task and point in time.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationWhat constitutes a trustworthy system differs between trustors and depends on their individual standards for system trustworthiness.
“What constitutes a trustworthy system differs between trustors and depends on their individual standards for system trustworthiness [70,103].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [70]
Not checked in the free run.
- cites [103]
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningWhat is high enough for a system's accuracy may be different for different trustors.
“What is high enough may be different for different trustors.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningA system's actual trustworthiness can be influenced by changes in the system characteristics and by changes in the individual standards.
“The AT of a system can be influenced by changes in the system characteristics and by changes in the individual standards.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningPerceived trustworthiness may consist of the same general facets and subfacets as actual trustworthiness, and since these are not observable, they must be inferred from manifest variables, i.e., observable cues.
“PT may consist of the same general facets (e.g., ability, integrity, benevolence), same subfacets (e.g., accuracy for ability), and since these are not observable, they must be inferred from manifest variables (i.e., observable cues).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
PremisecitationCues are pieces of evidence that presumably provide insight into the actual trustworthiness of a system.
“Cues are pieces of evidence that presumably provide insight into the AT of a system [18,85,136].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [18]
Not checked in the free run.
- cites [85]
Not checked in the free run.
- cites [136]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe facets that contribute to overall perceived trustworthiness are similar to those that contribute to overall actual trustworthiness.
“The facets that contribute to overall PT are similar to those that contribute to overall AT (see Figure 3).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningSingle cues may provide only narrow or even misleading insights into the actual trustworthiness of a system, and each cue is related to the actual trustworthiness to some degree.
“Single cues may provide only narrow or even misleading insights into the AT of a system, and each cue is related to the AT to some degree.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningThe facets of perceived trustworthiness represent the external evaluation, e.g., through researchers, and must not necessarily reflect the trustors' internal representation of their perceived trustworthiness.
“Note, that these facets rather represent the external evaluation (e.g., through researchers) and must not necessarily reflect the trustors' internal representation of their PT.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationTrustors could lower their individual standards, which would increase the system's actual trustworthiness.
“Trustors could also lower their individual standards, which would increase the system's AT [134].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [134]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningTrustors may not be able to mark some boxes after the assessment, or may be unsure about whether they should mark certain boxes.
“This means that trustors may not be able to mark (i.e., tick or cross out) some of the boxes after the assessment, or they may be unsure about whether they should mark certain boxes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationResearch has investigated how different cues are weighted and associated with trust in decision aids.
“In line with this, research has investigated how different cues are weighted and associated with trust in decision aids [23].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIn most cases, after the trustworthiness assessment, there is still uncertainty about the trustee's actual trustworthiness.
“In most cases, after the trustworthiness assessment, there is still uncertainty about the trustee's AT.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationcitationPerceived trustworthiness reflects the result of a trustor's assessment of the trustee's actual trustworthiness.
“PT reflects the result of a trustor's assessment of the trustee's AT [5,91].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5]
Not checked in the free run.
- cites [91]
Not checked in the free run.
May combine two claims.
PremisecitationFactors influencing cue detection include trustor's mood and emotions, attentional capacity, situation awareness, time pressure, and experience with a system.
“Possible factors influencing cue detection are trustor's mood and emotions [12,15,38,95], attentional capacity [49], situation awareness [34], time pressure [115], or their experience with a system [132].”
Quote in the paperNot a substantial claim0 of 6 sources checked- cites [12]
Not checked in the free run.
- cites [15]
Not checked in the free run.
- cites [38]
Not checked in the free run.
- cites [95]
Not checked in the free run.
- cites [49]
Not checked in the free run.
- cites [34]
Not checked in the free run.
May combine two claims.
PremisecitationPerceptions regarding system accuracy vary with the perceived difficulty of the system's task, implying that contextual information may influence cue utilization.
“For instance, perceptions regarding system accuracy vary with the perceived difficulty of the system's task, implying that contextual information may influence cue utilization [107].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [107]
Not checked in the free run.
May combine two claims.
InterpretationreasoningOn the trustor's side, cue detection and cue utilization determine how strongly cues relate to trustor's PT and thus affect the accuracy of the assessment of AT.
“On the trustor's side, cue detection and cue utilization determine how strongly cues relate to trustor's PT and thus affect the accuracy of the assessment of AT.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningCues may be uncorrelated with AT, although trustors may use them to form their PT.
“In some cases, cues may also be uncorrelated with AT, although trustors may use them to form their PT.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningAnthropomorphism may be a cue that is not related to the AT of a system, but it may be a cue that is often utilized by trustors and strongly influences the PT of the system.
“Thus, anthropomorphism may be a cue that is not related to the AT of a system, but it may be a cue that is often utilized by trustors and strongly influences the PT of the system.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningIn order to make an accurate and informed assessment, relevant cues must be available.
“In order to make an accurate and informed assessment, relevant cues must be available.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningAnthropomorphizing a system may increase trust, but trust may also become (more) inadequate due to an inaccurate trustworthiness assessment.
“As a consequence, anthropomorphizing a system may increase trust, but trust may also become (more) inadequate due to an inaccurate trustworthiness assessment”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenUser interface properties such as low contrast could make cue detection more difficult.
“Beyond that, user interface properties such as low contrast could make cue detection more difficult.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningThe macro-level trustworthiness assessment of a system can be viewed as a net of micro-level trustworthiness assessments of different trustors, each of which provides new trustworthiness cues to other trustors.
“To understand how the accuracy of a stakeholder's trustworthiness assessment of a system is influenced by the trustworthiness assessment of other stakeholders, we propose that the macro-level trustworthiness assessment of a system can be viewed as a net of microlevel trustworthiness assessments of different trustors, each of which provides new trustworthiness cues to other trustors (see Figure 5).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningTrustors can, but do not have to, exchange cues with each other.
“Trustors can, but do not have to, exchange cues with each other.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAutomation bias is a phenomenon associated with overtrust in systems.
“For instance, such effects have been shown in research on the automation bias as a phenomenon associated with overtrust in systems [108]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [108]
Not checked in the free run.
May combine two claims.
InterpretationcitationLittle domain-, task-, and system knowledge may lead to incorrect utilization of cues, which may lead to inadequate assessments of system's AT.
“In contrast, little domain-, task-, and system knowledge may lead to incorrect utilization of cues, which may lead to inadequate assessments of system's AT (e.g., assuming that a high-quality user interface or the logo of a popular company indicates high system accuracy [8,54,72]).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [8]
Not checked in the free run.
- cites [54]
Not checked in the free run.
- cites [72]
Not checked in the free run.
May combine two claims.
InterpretationreasoningAT is likely to differ between stakeholders assessing the same system, and the system characteristics contain as many ATs as there are trustors assessing the system.
“Since AT is likely to differ between stakeholders assessing the same system, the system characteristics contain as many ATs as there are trustors assessing the system.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAlgorithm aversion is a phenomenon associated with undertrust after seeing systems fail.
“and in research on algorithm aversion as a phenomenon associated with undertrust after seeing systems fail [27].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningATs of different trustors may overlap to varying degrees.
“ATs of different trustors may overlap to varying degrees (see Figure 5).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningStakeholders utilize cues, arrive at their PT based on these cues, and as a result may produce new cues for other stakeholders.
“These stakeholders utilize cues, arrive at their PT based on these cues, and as a result may produce new cues (e.g., certificates, system manuals, testimonials) for other stakeholders that assess system trustworthiness (see Figure 5).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe trustworthiness propagation process is not a pipeline, nor does it involve a strict hierarchy; rather, it takes place in a complex social network of stakeholders.
“It is not a pipeline, nor does it involve a strict hierarchy; rather, it takes place in a complex social network of stakeholders.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenEnd user's individual standards may often be implicit, but may become more explicit and specific as they interact with the systems.
“End user's individual standards may often be implicit, but may become more explicit and specific as they interact with the systems.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationEnd users play a special role in the trustworthiness propagation process because they repeatedly assess a system's AT by interacting with the system and have to decide whether a system is trustworthy in general and whether to rely on a single output.
“End users play a special role in the trustworthiness propagation process, because they a) repeatedly assess a system's AT by interacting with the system, and b) have to decide whether a system is trustworthy in general, but also whether they want to rely on a single output (i.e., end users have to decide whether a single system recommendation belongs to the 5% incorrect cases reported in the”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [18]
Not checked in the free run.
May combine two claims.
InterpretationreasoningStakeholders' individual standards shape their assessment of system trustworthiness and, consequently, their provision of secondary cues.
“Stakeholders' individual standards shape their assessment of system trustworthiness and, consequently, their provision of secondary cues.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningIf Bob heavily utilizes secondary cues provided by Alice, and if Alice's individual standards do not match Bob's, he might mistakenly believe that the system is trustworthy for him.
“If Bob heavily utilizes secondary cues provided by Alice, and if Alice's individual standards do not match Bob's, he might mistakenly believe that the system is trustworthy for him.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningSecondary cues are important in increasing the efficiency of the micro-level trustworthiness assessment process.
“Secondary cues are important in increasing the efficiency of the micro-level trustworthiness assessment process.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenDeployers' individual standards for trustworthiness will be tied to their business goal.
“Their individual standards for trustworthiness will be tied to their business goal.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationSystem designers will provide secondary cues such as documentation, a summary of training, validation and test data, accuracy metrics, limitations and capabilities of the system, information presented in the user interface, and model cards.
“Following their assessment and with reference to regulatory requirements, system designers will provide secondary cues such as a documentation [70], a summary of their training, validation and test data, accuracy metrics, limitations and capabilities of the system, information presented in the user interface, and model cards [100].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [70]
Not checked in the free run.
- cites [100]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenContinuous daily interactions allow end users to quickly adjust their weighting and utilization of cues.
“Presumably, continuous daily interactions allow end users to quickly adjust their weighting and utilization of cues.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationcitationSystem designers' individual standards for a trustworthy system might include stable performance, specific requirements in the labeling process, higher performance than the current state-of-the-art ADM system on a benchmark data set, and robustness.
“Designers' individual standards for a trustworthy system might include stable performance [39,48,110], combined with specific requirements in the labeling process, a higher performance than the current state-of-the-art ADM system on a benchmark data set [105,122], and robustness.”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [39]
Not checked in the free run.
- cites [48]
Not checked in the free run.
- cites [110]
Not checked in the free run.
- cites [105]
Not checked in the free run.
- cites [122]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningUsers may get better or worse at detecting and utilizing cues.
“Users may get better (e.g., due to more knowledge and experience) or worse (e.g., due to habituation and less awareness) at detecting and utilizing cues.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningEnd users may provide secondary cues to other end users.
“End users may provide secondary cues to other end users.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenMarket approval for ADM systems, especially of those used in high-risk contexts, is likely to be granted only if the system meets a list of requirements consistent with standards agreed upon within a particular institutionalized community of values.
“At least for the foreseeable future, market approval for ADM systems, especially of those used in high-risk contexts, is likely to be granted only if the system meets a list of requirements consistent with standards agreed upon within a particular institutionalized community of values.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe areas in the circle reflect the result of the utilization of the detected cues.
“Note that the areas in the circle reflect the result of the utilization of the detected cues.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThe order of cues may matter; one cue may undermine or defeat other cues, and cues may mutually reinforce or enhance each other's relevance.
“Furthermore, the order of cues may matter, one cue may undermine or defeat other cues, and cues may mutually reinforce or enhance each other's relevance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe trustor's propensity to trust can affect the position of the cone.
“The trustor's propensity to trust can affect the position of the cone.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningIf the likelihood or strength of expected negative outcomes increases, the dashed line moves clockwise, making it less likely that the trustor will actually engage in trusting behavior.
“If the likelihood or strength of expected negative outcomes increases, this could be represented by moving the dashed line clockwise, making it less likely that the trustor will actually engage in trusting behavior”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenAs the system is assessed, the area indicating the presence of trustworthiness begins to fill the circle clockwise at 12 o'clock, followed by the area indicating the absence of trustworthiness.
“As the system is assessed, the area indicating the presence of trustworthiness (light grey; checked boxes) begins to fill the circle clockwise at 12 o'clock, followed by the area indicating the absence of trustworthiness (dark grey; crossed out boxes).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Prior to all assessments, the circle is completely filled with uncertainty.
May combine two claims.
InterpretationreasoningIf perceptions indicating the presence of a system's trustworthiness do not fill enough space to reach the lower bound of the cone, the trustor will be unwilling to be vulnerable to the trustee, and thus will not trust.
“If perceptions indicating the presence of a system's trustworthiness do not fill enough space to reach the lower bound of the cone, the trustor will be unwilling to be vulnerable to the trustee, and thus will not trust (Figure 6C).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe weights of the cues reflect the subjective importance with which a cue influences PT.
“The weights of the cues reflect the subjective importance with which a cue influences PT.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationCalibrated trust has been defined as a match of a system's trustworthiness and a trustor's trust in the system.
“Calibrated trust has been defined as a match of a system's trustworthiness and a trustor's trust in the system (e.g., [80,82]).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [80]
Not checked in the free run.
- cites [82]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
InterpretationreasoningAs the areas indicating the presence or absence of trustworthiness become larger, uncertainty about the trustee decreases.
“As the areas indicating the presence or absence of trustworthiness become larger, uncertainty about the trustee decreases.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningAs the uncertainty and perceived absence of trustworthiness about the trustee increase, it becomes less likely that trustor will be willing to make themselves vulnerable to the trustee.
“As the uncertainty and perceived absence of trustworthiness about the trustee increase, it becomes less likely that trustor will be willing to make themselves vulnerable to the trustee.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningDuring utilization, the trustor weights the detected cues, which then influence perceptions regarding the presence or absence of trustworthiness.
“During utilization, the trustor weights the detected cues, which then influence perceptions regarding the presence or absence of trustworthiness.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe use of cues is a nonlinear, complex matter.
“The details of this function (e.g., which cues are more important than others, how cues are combined) are ultimately empirical questions, but we suggest that the use of cues is a nonlinear, complex matter.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningRed flag cues could strongly increase the perceived absence of trustworthiness while decreasing the proportion of perceived presence of trustworthiness.
“For example, there may be red flag cues that could strongly increase the perceived absence of trustworthiness while decreasing the proportion of perceived presence of trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningAs the area indicating the presence of trustworthiness becomes larger, it becomes more likely that the trustor is willing to accept the remaining uncertainty and any perceived absence of trustworthiness.
“As the area indicating the presence of trustworthiness becomes larger, it becomes more likely that the trustor is willing to accept the remaining uncertainty and any perceived absence of trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe accuracy of the trustworthiness assessments has strong implications for whether trust and potential trusting behavior are well-informed and based on adequate expectations given the trustor's individual standards.
“Given the relationships described in the previous paragraphs, the accuracy of the trustworthiness assessments has strong implications for whether trust and potential trusting behavior are well-informed and based on adequate expectations given the trustor's individual standards.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe degree of accuracy is the result of the comparison between a trustor's PT and a system's AT.
“The degree of accuracy is the result of the comparison between a trustor's PT and a system's AT.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe proportions of accurate and inaccurate assessments reflect how accurately a trustor's PT reflects the system's AT.
“The proportions of accurate and inaccurate assessments reflect how accurately a trustor's PT reflects the system's AT.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningIf the area representing the perceived presence of trustworthiness passes the lower bound of the cone, the trustor is willing to make themselves vulnerable and is in a position to trust.
“That means if the area of the circle that is filled with perceptions indicating the presence of trustworthiness passes the lower bound of the cone, the trustor is willing to make themselves vulnerable -the trustor is in a position to trust.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCalibrated trust has received much attention, but also increasing criticism, due to the lack of a clear definition and a clear way to measure it.
“This concept has received much attention, but also increasing criticism, due to the lack of a clear definition and a clear way to measure it (for a recent review, see [144]).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [144]
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe TrAM complements existing trust models by adding the trustee and its characteristics, specifying the concepts relevant to the trustworthiness assessment, describing their relations, and emphasizing the role of PT in the overall trust development process.
“In this paper, we describe the TrAM, which complements existing trust models (e.g., [82,91]) by adding the trustee (and its characteristics), specifying the concepts relevant to the trustworthiness assessment (system characteristics, individual standards, AT, and PT), describing their relations, and emphasizing the role of PT in the overall trust development process.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [82]
Not checked in the free run.
- cites [91]
Not checked in the free run.
May combine two claims.
InterpretationreasoningIf a single cue in the trustworthiness propagation process is irrelevant, based on an inaccurate assessment, reflects intentional inaccuracy, or if the stakeholder who produced it has individual standards inconsistent with other stakeholders, the trustworthiness assessment of a large number of stakeholders may become inaccurate.
“If this cue is irrelevant, based on an inaccurate assessment, reflects intentional inaccuracy (i.e., misinformation), or if the stakeholder who has produced this cue has individual standards that are not consistent with those of the rest of the stakeholders, the trustworthiness assessment of a large number of stakeholders may become inaccurate.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationEnvironmental changes may affect the actual trustworthiness of a system, and this may also affect the relevance of a cue.
“For instance, considering that environmental changes may affect the AT of a system, this may also affect the relevance of a cue [55].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [55]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenAn inaccurate trustworthiness assessment could erroneously increase or decrease perceived trustworthiness and thereby increase the likelihood of expectation violation or perceived betrayal of the trustee.
“An inaccurate trustworthiness assessment could erroneously increase or decrease the perceived trustworthiness and thereby increase the likelihood of expectation violation or even perceived betrayal of the trustee.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenFactors that influence the accuracy of the trustworthiness assessment and the uncertainty about the trustee are relevance and availability of cues on the system side, and detection and utilization of cues on the trustor's side.
“Specifically, factors that influence the accuracy of the trustworthiness assessment and the uncertainty about the trustee are relevance and availability of cues on the system side, and detection and utilization of cues on the trustor's side.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThree factors could be calibrated to achieve an optimal human-system interaction: the accuracy of the trustworthiness assessment, the uncertainty about the trustee, and the trustor's threshold for their willingness to be vulnerable.
“In Figure 6, we have identified three factors that could be calibrated to achieve an”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningA highly accurate trustworthiness assessment provides a solid basis for well-informed and appropriate decisions in further stages of the trust development process.
“A highly accurate trustworthiness assessment then provides a solid basis for well-informed and appropriate decisions in further stages of the trust development process (e.g., well-informed decisions about whether to engage in trusting behavior).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningMaking relevant cues may determine the accuracy of trustors' trustworthiness assessment.
“With respect to the trust development process, making relevant cues may determine the accuracy of their trustworthiness assessment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe accuracy of the trustworthiness assessment is a prerequisite to whatever calibrated trust is.
“We propose that the accuracy of the trustworthiness assessment is a prerequisite to whatever calibrated trust is.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningA strong match between AT and PT can more simply be described as a high accuracy of the trustworthiness assessment.
“We propose that a strong match between AT and PT can more simply be described as a high accuracy of the trustworthiness assessment.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningTrustors who have clear and explicit individual standards may be better able to compare different systems, as well as compare their own standards to a system's actual trustworthiness.
“Trustors who have clear and explicit individual standards, may be better able to compare different systems, as well as compare their own standards to system's AT.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningIt may be more appropriate to assume that AT is assessed and leads to PT, and that PT then affects whether people will be in a position to trust and to engage in trusting behavior.
“It may be more appropriate to assume that AT is assessed and leads to PT, and that PT then affects whether people will be in a position to trust and to engage in trusting behavior.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningKnowing which cues are relevant would enable the design of systems tailored to the trustor's individual standards for trustworthiness.
“Knowing which cues are relevant would enable the design of systems that are tailored to the trustor's individual standards for trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningThe micro-level trustworthiness assessment process implies that, in order to achieve an accurate assessment of a system's actual trustworthiness, relevant cues must be made available on the system's side.
“The micro-level trustworthiness assessment process implies that, in order to achieve an accurate assessment of a system's AT, relevant cues must be made available on the system's side.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationIndividual standards for trustworthiness may differ between stakeholders, and if these differences are not explicit, incorrect assumptions about cues may lead to inadequate utilization and thus low accuracy in the assessment of actual trustworthiness.
“Individual standards for trustworthiness may differ between the stakeholders (see also [70]), and if these differences are not explicit, incorrect assumptions about cues may lead to an inadequate utilization and thus low accuracy in the assessment of AT.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [70]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningPower imbalance, both interinstitutional and interpersonal, may be an important factor in anticipating the trustworthiness assessment process.
“What might be an important factor in anticipating the trustworthiness assessment process is power imbalance -interinstitutional and interpersonal.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationreasoningAn interinstitutional power imbalance might occur between deployers and users of the system, or between earlier trustors and later trustors within the trustworthiness propagation process.
“An interinstitutional power imbalance might occur between deployers and users of the system, or between earlier trustors and later trustors within the trustworthiness propagation process.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe accuracy of the trustworthiness assessment may increase when different trustors providing secondary cues make their individual standards explicit and available for scrutiny by others.
“So, it may be possible that the accuracy of the trustworthiness assessment increases when different trustors providing secondary cues make their individual standards explicit and available for scrutiny by others.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationKnowing which cues are frequently utilized and heavily weighted by users to assess system trustworthiness can influence system design by adapting cues to make them more adequately utilized.
“Knowing which cues are frequently utilized and heavily weighted by users to assess system trustworthiness can further influence system design by adapting cues to make them more adequately utilized (e.g., decreasing saliency of cues that are frequently utilized incorrectly) [30].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30]
Not checked in the free run.
May combine two claims.
InterpretationcitationGiven the relative nature of trustworthiness, trustors who see a trustworthy AI label may mistakenly assume that their individual standards are met, and may not examine the system in detail, which could lead to an inaccurate trustworthiness assessment.
“Given the relative nature of trustworthiness, trustors who see this label may mistakenly assume that their individual standards are met, and may not examine the system in detail [118], which could lead to an inaccurate trustworthiness assessment.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [118]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenIn hierarchical work environments, supervisors may give less weight to, or not even detect, cues provided by employees using new systems.
“Conversely, in hierarchical work environments, supervisors may give less weight to, or not even detect, cues provided by employees using new systems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningCommunication about trustworthy systems may need to emphasize the relative and individual nature of trustworthiness.
“A final implication of our model is that communication about”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningCues provided by supervisors or higher-level management may be weighted more heavily by employees.
“Cues provided by supervisors or higher-level management may be weighted more heavily by employees.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenInterpersonal power imbalances may arise in hierarchical work environments.
“Interpersonal power imbalances may arise in hierarchical work environments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIndividual standards may affect trustors' detection of cues, as they may influence what trustors are looking for in a system.
“Individual standards may also affect trustors' detection of cues, as they may influence what trustors are looking for in a system.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenTrustors may initially not be able to communicate their individual standards, or may not be aware of the cues they have used in assessing a system's trustworthiness.
“For instance, trustors may initially not be able to communicate their individual standards, or may not be aware of the cues they have used in assessing a system's trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningPreviously proposed trustworthiness facets may reflect issues that are important to all trustors' individual standards.
“For example, previously proposed trustworthiness facets may reflect issues that are important to all trustors' individual standards.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationExplainable AI research develops transparent models where rules provide relevant cues, or posthoc explainability approaches that try to generate relevant cues by analyzing the decision processes of a black-box model.
“For example, XAI research develops transparent models, e.g., models based on simple rules, where these rules provide relevant cues, or posthoc explainability approaches that try to generate relevant cues by analyzing the decision processes of a black-box model [11,67,86,99,145].”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [11]
Not checked in the free run.
- cites [67]
Not checked in the free run.
- cites [86]
Not checked in the free run.
- cites [99]
Not checked in the free run.
- cites [145]
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenIndividual standards are not arbitrary but relate to a common understanding of trustworthiness.
“It is important to emphasize that we do not expect these standards to be arbitrary, but rather expect them to relate to a common understanding of trustworthiness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningIn cases where accurate trustworthiness assessments are critical, it will be necessary to simultaneously optimize for the availability of relevant cues and for system performance.
“This discussion also implies that in cases where accurate trustworthiness assessments are critical, it will be necessary to simultaneously optimize for the availability of relevant cues and for system performance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningLabels such as trustworthy AI labels can help people quickly assess whether a product meets their individual standards, a process that would otherwise require a resource-intensive trustworthiness assessment process.
“We are not saying that such labels and labels are always misleading -they can help people to quickly assess whether a product meets their individual standards, a process that would otherwise require a resource-intensive (trustworthiness) assessment process.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningCalling a label trustworthy AI may lead to different expectations among different trustors and should therefore be accompanied by a summary of the individual standards that were relevant to a particular institution that assessed the trustworthiness of a system.
“However, we do say that calling a label”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationExplainable AI research aims to make relevant cues available.
“In the terminology of our model, this research aims to make relevant cues available [78].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [78]
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThere is overlap in individual standards across trustors, but the exact combination of features, as well as their weight in forming perceived trustworthiness, may be idiosyncratic.
“Accordingly, we expect overlap in the individual standards across trustors, but also that the exact combination of features, as well as their weight in forming PT, may be idiosyncratic.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe suggestion that relevance and availability of cues are central to the accurate assessment of system trustworthiness provides an argument for the importance of research in explainable AI.
“The suggestion that relevance and availability of cues are central to the accurate assessment of system trustworthiness also provides an argument for the importance of research in the area of explainable AI (XAI).”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationnone givenIndividual standards of system trustworthiness differ across trustors and are task- and context-specific.
“Second, we claim that individual standards of system trustworthiness differ across trustors and are task-and context-specific.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningAn accurate assessment of system trustworthiness is a prerequisite for adequate and informed trust and trusting behavior.
“An accurate assessment of system trustworthiness is therefore a prerequisite for adequate and informed trust and trusting behavior.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Interpretationnone givenThe Trustworthiness Assessment Model extends existing trust models by adding the trustee and its characteristics.
“In doing so, we explicate a process that focuses on trustworthiness as the basis for trust, and a model that extends existing trust models by adding the trustee and its characteristics.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe model makes assumptions about theoretical processes and concepts that are currently difficult to capture and that may not be readily accessible to the stakeholders involved.
“First, the model makes assumptions about theoretical processes and concepts that are currently difficult to capture and that may not be readily accessible to the stakeholders involved.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenEach concept in the model requires empirical evaluation and interdisciplinary theoretical refinement.
“Each concept in our model requires empirical evaluation and interdisciplinary theoretical refinement -something we hope will happen in response to the ideas we present in this paper.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenThe safe and effective use of ADM systems requires an accurate assessment of the system, its strengths and weaknesses against a trustor's goals and standards.
“The safe and effective use of ADM systems requires an accurate assessment of the system, its strengths and weaknesses against a trustor's goals and standards.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenThe authors do not expect trustors to always explicitly engage in cue detection and utilization, nor may they be motivated to engage in such a process.
“We do not expect trustors to always explicitly engage in cue detection and utilization, nor may they be motivated to engage in such a process.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationnone givenOn the trustor's side, factors that influence the accuracy of trustworthiness assessments include the trustors' individual standards and the detection and utilization of cues.
“On the trustor's side, factors that influence the accuracy of trustworthiness assessments include the trustors' individual standards and the detection and utilization of cues.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningEffective human oversight requires an accurate trustworthiness assessment.
“Thus, effective human oversight requires an accurate trustworthiness assessment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationIf we want effective human oversight, we need to focus on making relevant cues available.
“In line with other research [43], our model suggests that if we want effective human oversight, we need to focus on making relevant cues available.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [43]
Not checked in the free run.
The paper relies on this without support.
There is little or no research explicitly describing the trustworthiness assessment process and the factors that influence the accuracy of trustworthiness assessments.
“Despite its importance, to date there has been little research on the trustworthiness assessment process.”
“However, to the best of our knowledge, there is no research that explicitly describes the trustworthiness assessment process and the factors that influence the accuracy of trustworthiness assessments.”
The authors state this as an assumption.
Perceptions of trustworthiness and intentions of trust influence behavior.
“Nevertheless, if we assume that perceptions (i.e., of trustworthiness) and intentions (i.e., trust) influence behavior, then trustworthiness, trust, and trusting behavior are expected to be positively related [135].”
The authors state this as an assumption.
Prior to all assessments, the circle is completely filled with uncertainty.
“We assume that prior to all assessments, the circle is completely filled with uncertainty (blue; empty boxes in the requirement list metaphor).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The objective metrics used are commonly adapted in literature to capture the level of reliance within the human-AI interaction context.
“These parameters are commonly adapted in literature to capture the level of reliance within the human-AI interaction context.”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [113]
Section 5.2 ('On calibrated trust'; page unavailable) says researchers have commonly measured calibrated trust by observing whether people accept or reject system outputs. Section 2 identifies reliance as a form of trusting behavior. These passages support the use of behavioral observations in human–AI research, but the source does not identify the citing sentence’s specific parameters or say they are commonly used to measure the level of reliance. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736 · see citing works
Not So Different After All: A Cross-Discipline View Of Trust
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998) · Academy of Management Review · doi:10.5465/amr.1998.926617 · Cited by 10,274 · see citing works
Affect and Cognition Based Trust as Foundations for Interpersonal Cooperation in Organizations
McAllister, D. J. (1995) · Academy of Management Journal · doi:10.2307/256727 · Cited by 7,346 · see citing works
Magnitude and complex based diffusion signal reconstruction
(2014) · HAL · doi:10.1007/978-3-319 · Cited by 6,731 · see citing works
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · see citing works
Bower, G. H. (1981) · American Psychologist · doi:10.1037/0003-066x.36.2.129 · Cited by 4,154 · see citing works
Rosenthal, R., & Jacobson, L. (1968) · The Urban Review · doi:10.1007/bf02322211 · Cited by 3,878 · see citing works
Deciding advantageously before knowing the advantageous strategy
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997) · Science · doi:10.1126/science.275.5304.1293 · Cited by 3,547 · see citing works
Mood and judgment: The affect infusion model (AIM).
Forgas, J. P. (1995) · Psychological Bulletin · doi:10.1037/0033-2909.117.1.39 · Cited by 3,470 · see citing works
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · see citing works
Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.49.1.95 · Cited by 3,146 · see citing works
The “false consensus effect”: An egocentric bias in social perception and attribution processes
Ross, L., Greene, D., & House, P. (1977) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(77)90049-x · Cited by 3,139 · see citing works
Perception and the Representative Design of Psychological Experiments
Brunswik, E. (1956) · doi:10.1525/9780520350519 · Cited by 2,974 · see citing works
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
Deutsch, M. (1975) · Journal of Social Issues · doi:10.1111/j.1540-4560.1975.tb01000.x · Cited by 2,783 · see citing works
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · see citing works
Colquitt, J. A., Scott, B. A., & LePine, J. A. (2007) · Journal of Applied Psychology · doi:10.1037/0021-9010.92.4.909 · Cited by 2,606 · see citing works
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015 · see citing works
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018) · Nature · doi:10.1038/s41586-018-0637-6 · Cited by 1,859 · see citing works
Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799 · see citing works
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · see citing works
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.01943 · Cited by 1,581 · see citing works
The theoretical status of latent variables.
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003) · Psychological Review · doi:10.1037/0033-295x.110.2.203 · Cited by 1,566 · see citing works
Trust, control strategies and allocation of function in human-machine systems
LEE, J., & MORAY, N. (1992) · Ergonomics · doi:10.1080/00140139208967392 · Cited by 1,459 · see citing works
Human cognitive abilities: A survey of factor-analytic studies
Eysenck, H. J. (1994) · Personality and Individual Differences · doi:10.1016/0191-8869(94)90126-0 · Cited by 1,392 · see citing works
On the accuracy of personality judgment: A realistic approach
Funder, D. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.4.652 · Cited by 1,342 · see citing works
The role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00038-7 · Cited by 1,276 · see citing works
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. V. D., Yuille, A. L., & He, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00059 · Cited by 940 · see citing works
From Here to Autonomy: Lessons Learned From Human–Automation Research
Endsley, M. R. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816681350 · Cited by 898 · see citing works
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895 · see citing works
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768 · see citing works
Trust between humans and machines, and the design of decision aids
Muir, B. M. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80013-5 · Cited by 767 · see citing works
Addressing bias in artificial intelligence in health care
Parikh, R. B., Teeple, S., & Navathe, A. S. (2019) · JAMA · doi:10.1001/jama.2019.18058 · Cited by 764 · see citing works
Human judgment and social policy: irreducible uncertainty, inevitable error, unavoidable injustice
(1997) · Choice Reviews Online · doi:10.5860/choice.34-4545 · Cited by 717 · see citing works
Measuring trust inside organisations
Dietz, G., & Den Hartog, D. N. (2006) · Personnel Review · doi:10.1108/00483480610682299 · Cited by 596 · see citing works
Similarities and differences between human–human and human–automation trust: an integrative review
Madhavan, P., & Wiegmann, D. A. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500337708 · Cited by 577 · see citing works
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Jacovi, A., Marasović, A., Miller, T., & Goldberg, Y. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445923 · Cited by 545 · see citing works
Trust: Reason, Routine, Reflexivity
Kidd, J. (2006) · Knowledge Management Research & Practice · doi:10.1057/palgrave.kmrp.8500105 · Cited by 543 · see citing works
Expanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538 · see citing works
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300509 · Cited by 527 · see citing works
Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5285 · Cited by 466 · see citing works
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams
de Visser, E. J., Peeters, M. M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., & Neerincx, M. A. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-019-00596-x · Cited by 447 · see citing works
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942288 · Cited by 434 · see citing works
The benefits of imperfect diagnostic automation: a synthesis of the literature
Wickens, C. D., & Dixon, S. R. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500370105 · Cited by 423 · see citing works
Trust in Artificial Intelligence: Meta-Analytic Findings
Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211013988 · Cited by 397 · see citing works
The impact of interface usability on trust in Web retailers
Roy, M. C., Dewit, O., & Aubert, B. A. (2001) · Internet Research · doi:10.1108/10662240110410165 · Cited by 395 · see citing works
Establishing the rules for building trustworthy AI
Floridi, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0055-y · Cited by 394 · see citing works
The Perceived Utility of Human and Automated Aids in a Visual Detection Task
Dzindolet, M. T., Pierce, L. G., Beck, H. P., & Dawe, L. A. (2002) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/0018720024494856 · Cited by 386 · see citing works
Laux, J., Wachter, S., & Mittelstadt, B. (2024) · Regulation & Governance · doi:10.1111/rego.12512 · Cited by 365 · see citing works
I Trust It, but I Don’t Know Why
Merritt, S. M., Heimbaugh, H., LaChapell, J., & Lee, D. (2013) · Hum. Factors · doi:10.1177/0018720812465081 · Cited by 354 · see citing works
Langer, M., & Landers, R. N. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106878 · Cited by 338 · see citing works
Self-citationPrecision-Recall versus Accuracy and the Role of Large Data Sets
Juba, B., & Le, H. S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014039 · Cited by 331 · see citing works
Theoretical Considerations and Development of a Questionnaire to Measure Trust in Automation
Körber, M. (2019) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-96074-6_2 · Cited by 317 · see citing works
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
Buçinca, Z., Lin, P., Gajos, K. Z., & Glassman, E. L. (2020) · Proceedings of the 25th International Conference on Intelligent User Interfaces · doi:10.1145/3377325.3377498 · Cited by 307 · see citing works
Trusting Automation: Designing for Responsivity and Resilience
Chiou, E. K., & Lee, J. D. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211009995 · Cited by 301 · see citing works
Learning from streaming data with concept drift and imbalance: an overview
Hoens, T. R., Robi Polikar, A., & Chawla, N. V. (2012) · Progress in Artificial Intelligence · doi:10.1007/s13748-011-0008-0 · Cited by 301 · see citing works
(1955) · Nature · doi:10.1038/1761009b0 · Cited by 298 · see citing works
The relationship between trust in AI and trustworthy machine learning technologies
Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G., & van Moorsel, A. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372834 · Cited by 296 · see citing works
The essential brunswik: Beginnings, explications, applications.
Brunswik, E., Hammond, K. R., & Stewart, T. R. (2001) · Cited by 292 · see citing works
Kocielnik, R., Amershi, S., & Bennett, P. N. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300641 · Cited by 277 · see citing works
Dietvorst, B. J., & Bharti, S. (2020) · Psychological Science · doi:10.1177/0956797620948841 · Cited by 275 · see citing works
Kelton, K., Fleischmann, K. R., & Wallace, W. A. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20722 · Cited by 274 · see citing works
Trust: An Integrative Review from a Person–Situation Perspective
Thielmann, I., & Hilbig, B. E. (2015) · Review of General Psychology · doi:10.1037/gpr0000046 · Cited by 266 · see citing works
The flaws of policies requiring human oversight of government algorithms
Green, B. (2022) · Computer Law & Security Review · doi:10.1016/j.clsr.2022.105681 · Cited by 263 · see citing works
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2021) · Communications of the ACM · doi:10.1145/3433949 · Cited by 262 · see citing works
Affective Processes in Human–Automation Interactions
Merritt, S. M. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811411912 · Cited by 260 · see citing works
How transparency modulates trust in artificial intelligence
Zerilli, J., Bhatt, U., & Weller, A. (2022) · Patterns · doi:10.1016/j.patter.2022.100455 · Cited by 257 · see citing works
Visual attention modulates signal detectability.
Hawkins, H. L., Hillyard, S. A., Luck, S. J., et al. (1990) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.16.4.802 · Cited by 250 · see citing works
Adaptive trust calibration for human-AI collaboration
Okamura, K., & Yamada, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229132 · Cited by 239 · see citing works
How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
Vereschak, O., Bailly, G., & Caramiaux, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476068 · Cited by 229 · see citing works
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
Oliveira, J., Renna, F., Costa, P. D., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3137048 · Cited by 215 · see citing works
Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211 · see citing works
Essentials of business communication
Guffey, M. E. (1988) · Cited by 196 · see citing works
Change blindness and inattentional blindness
Jensen, M. S., Yao, R., Street, W. N., & Simons, D. J. (2011) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.130 · Cited by 175 · see citing works
Designing for Responsible Trust in AI Systems: A Communication Perspective
Liao, Q. V., & Sundar, S. S. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533182 · Cited by 165 · see citing works
Test Review: Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV)
Kaufman, A. S., Flanagan, D. P., Alfonso, V. C., & Mascolo, J. T. (2006) · Journal of Psychoeducational Assessment · doi:10.1177/0734282906288389 · Cited by 164 · see citing works
Human, L. J., & Biesanz, J. C. (2013) · Personality and Social Psychology Review · doi:10.1177/1088868313495593 · Cited by 161 · see citing works
Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2022.103839 · Cited by 161 · see citing works
GLocalX—From Local to Global Explanations of Black Box AI Models
Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103457 · Cited by 140 · see citing works
"General Intelligence" Objectively Determined and Measured.
Spearman, C. (1961) · Appleton-Century-Crofts eBooks · doi:10.1037/11491-006 · Cited by 138 · see citing works
Langer, M., König, C. J., Back, C., & Hemsing, V. (2022) · Journal of Business and Psychology · doi:10.1007/s10869-022-09829-9 · Cited by 136 · see citing works
Self-citationZ-Inspection <sup>®</sup> : A Process to Assess Trustworthy AI
Zicari, R. V., Brodersen, J., Brusseau, J., Dudder, B., Eichhorn, T., Ivanov, T., Kararigas, G., Kringen, P., McCullough, M., Moslein, F., et al. (2021) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2021.3066209 · Cited by 134 · see citing works
Measuring Individual Differences in the Perfect Automation Schema
Merritt, S. M., Unnerstall, J. L., Lee, D., & Huber, K. (2015) · Hum. Factors · doi:10.1177/0018720815581247 · Cited by 119 · see citing works
The Volkswagen emissions scandal and its aftermath
Jung, J. C., & Sharon, E. (2019) · Global Business and Organizational Excellence · doi:10.1002/joe.21930 · Cited by 109 · see citing works
A Design Methodology for Trust Cue Calibration in Cognitive Agents
de Visser, E., Cohen, M., Freedy, A., & Parasuraman, R. (2014) · Interacción · doi:10.1007/978-3-319-07458-0_24 · Cited by 107 · see citing works
The Effects of Time Pressure and Experience on Nurses' Risk Assessment Decisions
Thompson, C. A., Dalgleish, L. I., Bucknall, T. K., Estabrooks, C. A., Hutchinson, A. M., Fraser, K., de Vos, R., Binnekade, J. M., Barrett, G., & Saunders, J. (2008) · Nursing Research · doi:10.1097/01.nnr.0000313504.37970.f9 · Cited by 103 · see citing works
How to achieve trustworthy artificial intelligence for health.
Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237289 · Cited by 103 · see citing works
Langer, M., König, C. J., & Busch, V. (2020) · Journal of Business and Psychology · doi:10.1007/s10869-020-09711-6 · Cited by 103 · see citing works
Self-citationSchelble, B. G., Lopez, J., Textor, C., Zhang, R., McNeese, N. J., Pak, R., & Freeman, G. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208221116952 · Cited by 99 · see citing works
Rechkemmer, A., & Yin, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501967 · Cited by 97 · see citing works
Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial
Thompson, W. R., Reinisch, A. J., Unterberger, M. J., et al. (2018) · Pediatric Cardiology · doi:10.1007/s00246-018-2036-z · Cited by 92 · see citing works
The Measurement of the Propensity to Trust Automation
Jessup, S. A., Schneider, T. R., Alarcon, G. M., Ryan, T. J., & Capiola, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-21565-1_32 · Cited by 90 · see citing works
Serna, I., Morales, A., Fiérrez, J., & Obradovich, N. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103682 · Cited by 88 · see citing works
The Sanction of Authority: Promoting Public Trust in AI
Knowles, B., & Richards, J. T. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445890 · Cited by 86 · see citing works
Effects of specialization in computers, web sites, and web agents on e-commerce trust
Koh, Y. J., & Sundar, S. S. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2010.08.002 · Cited by 84 · see citing works
Merritt, S. M., Lee, D., Unnerstall, J. L., & Huber, K. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814561675 · Cited by 83 · see citing works
Examining the effect of explanation on satisfaction and trust in AI diagnostic systems
Alam, L., & Mueller, S. T. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01542-6 · Cited by 82 · see citing works
Kirlik, A. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195374827.001.0001 · Cited by 66 · see citing works
Kay, M., Patel, S., & Kientz, J. A. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702603 · Cited by 59 · see citing works
Textor, C., Zhang, R., Lopez, J., Schelble, B. G., McNeese, N. J., Freeman, G., Pak, R., Tossell, C. C., & de Visser, E. J. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221113964 · Cited by 58 · see citing works
Human Performance Consequences of Automated Decision Aids: The Impact of Time Pressure
Rieger, T., & Manzey, D. (2020) · Hum. Factors · doi:10.1177/0018720820965019 · Cited by 53 · see citing works
Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study
Scharowski, N., Benk, M., Kühne, S. J., Wettstein, L., & Brühlmann, F. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3593994 · Cited by 53 · see citing works
Artificial agents’ explainability to support trust: considerations on timing and context
Papagni, G., de Pagter, J., Zafari, S., Filzmoser, M., & Koeszegi, S. T. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01462-7 · Cited by 52 · see citing works
Prewett, P. N. (1995) · Psychological Assessment · doi:10.1037/1040-3590.7.1.69 · Cited by 51 · see citing works
Modeling and Guiding the Creation of Ethical Human-AI Teams
Flathmann, C., Schelble, B. G., Zhang, R., & McNeese, N. J. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462573 · Cited by 43 · see citing works
Challenging presumed technological superiority when working with (artificial) colleagues
Rieger, T., Roesler, E., & Manzey, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-07808-x · Cited by 39 · see citing works
Review of validity research on the Stanford-Binet Intelligence Scale: Fourth Edition.
Laurent, J., Swerdlik, M. E., & Ryburn, M. (1992) · Psychological Assessment · doi:10.1037/1040-3590.4.1.102 · Cited by 37 · see citing works
Baer, M. D., & Colquitt, J. A. (2018) · Routledge eBooks · doi:10.4324/9781315745572-12 · Cited by 36 · see citing works
Bahner, J. E., Elepfandt, M. F., & Manzey, D. (2008) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120805201906 · Cited by 35 · see citing works
When, How, and Why Do We Trust Technology Too Much?
Hardré, P. L. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801873-6.00005-4 · Cited by 30 · see citing works
(Over)Trust in Automated Driving
Kundinger, T., Wintersberger, P., & Riener, A. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3312869 · Cited by 30 · see citing works
From the ground up: developing a practical ethical methodology for integrating AI into industry
Anderson, M. M., & Fort, K. (2022) · AI & Society · doi:10.1007/s00146-022-01531-x · Cited by 29 · see citing works
Distinct, but top-down modulable color and positional priming mechanisms in visual pop-out search
Geyer, T., & Müller, H. J. (2008) · Psychological Research · doi:10.1007/s00426-008-0207-x · Cited by 26 · see citing works
Towards Warranted Trust: A Model on the Relation Between Actual and Perceived System Trustworthiness
Schlicker, N., & Langer, M. (2021) · Mensch und Computer (MuC) · doi:10.1145/3473856.3474018 · Cited by 26 · see citing works
Self-citationTrustworthy AI Services in the Public Sector: What Are Citizens Saying About It?
Drobotowicz, K., Kauppinen, M., & Kujala, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73128-1_7 · Cited by 25 · see citing works
How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes
Papenmeier, A., Kern, D., Hienert, D., Kammerer, Y., & Seifert, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501915 · Cited by 25 · see citing works
Wei, Q., Zeng, S., Wang, L., Yan, Y., Wang, T., Xu, J., Zhang, M., Lv, W., Dietrich, C. F., & Cui, X. (2021) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15816 · Cited by 21 · see citing works
Estimating and Improving Fairness with Adversarial LearningPreprint
Li, X., Cui, Z., Wu, Y., Gu, L., & Harada, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04243 · Cited by 20 · see citing works
Uncertain Context: Uncertainty Quantification in Machine Learning
Jalaian, B. A., Lee, M., & Russell, S. (2019) · AI Magazine · doi:10.1609/aimag.v40i4.4812 · Cited by 17 · see citing works
Lee, M. A., Alarcon, G. M., & Capiola, A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.797443 · Cited by 15 · see citing works
Judgment and Decision Making in Staffing Research and Practice
Kuncel, N. R. (2017) · SAGE Publications Ltd eBooks · doi:10.4135/9781473914940.n17 · Cited by 14 · see citing works
Assuring Fairness of Algorithmic Decision Making
Hauer, M. P., Adler, R., & Zweig, K. A. (2021) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw52544.2021.00029 · Cited by 10 · see citing works
Towards an Accountable and Reproducible Federated Learning: A FactSheets ApproachPreprint
Baracaldo, N., Anwar, A., Purcell, M., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.12443 · Cited by 10 · see citing works
Creating Order Amidst Food Eco-Label Chaos
Czarnezki, J. J., Homan, A., & Jeans, M. (2015) · DigitalCommons@Pace (Pace University) · Cited by 9 · see citing works
What the Hack Is Wrong with Software Doping?
Baum, K. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47169-3_49 · Cited by 9 · see citing works
Self-citationWischnewski, M., Krämer, N. C., & Müller, E. (2023) · doi:10.31234/osf.io/qt86s · Cited by 8 · see citing works
Conway, D., Chen, F., Yu, K., Zhou, J. J., & Morris, R. W. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2892433 · Cited by 4 · see citing works
Training one model to detect heart and lung sound events from single point auscultationsPreprint
Melms, L., Ileșan, R. R., Köhler, U., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.06078 · Cited by 4 · see citing works
Attention Guidance Agents with Eye-Tracking - A Use-Case Based on the MATBII Cockpit Task
Durant, S., Wilkins, B., Woods, C., Uliana, E., & Stathis, K. (2021) · EMAS@AAMAS · doi:10.1007/978-3-030-97457-2_6 · Cited by 3 · see citing works
Cancro, G., Pan, S., & Foulds, J. R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.02987 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. 2017. Parseval networks: Improving robustness to adversarial examples. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), PMLR, 854-863. Retrieved from https://proceedings.mlr.press/v70/cisse17a.html
- Maria Madsen and Shirley Gregor. 2000. Measuring human-computer trust. In 11th australasian conference on information systems, Citeseer, 6-8.
- Katharina Morik, Helena Kotthaus, Lukas Heppe, Danny Heinrich, Raphael Fischer, Andreas Pauly, and Nico Piatkowski. 2021. The care label concept: a certification suite for trustworthy and resource-aware machine learningThe Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning. ArXiv210600512 Cs (June 2021). Retrieved June 2, 2021 from http://arxiv.org/abs/2106.00512 · arXiv
- Klaus Rüdiger, Francesca Cabbidu, Sissy-Christin Lorenz, and Holger Hartman. 2022. Intercultural or international perspectives in human resource management. In Cross-Cultural Business Conference 2022, Austria, 154-164.
- Ewart J. de Visser, Marvin Cohen, Amos Freedy, and Raja Parasuraman. 2014. A design methodology for trust cue calibration in cognitive agents. In Virtual, Augmented and Mixed Reality. Designing and Developing Virtual and Augmented Environments, Randall Shumaker and Stephanie Lackey (eds.). Springer International Publishing, Cham, 251-262. DOI:https://doi.org/10.1007/978-3-319-07458-0_24 · DOI
- Lisa van der Werff, Kirsimarja Blomqvist, and Sirpa Koskinen. 2021. Trust Cues in Artificial Intelligence: A Multilevel Case Study in a Service Organization. In Understanding Trust in Organizations. Routledge.
Cited by
Sorted by citations
Salimzadeh, S., He, G., & Gadiraju, U. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641905 · Cited by 41 · see citing works
Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3593978 · Cited by 37 · see citing works
- Authors
- Schlicker, N., Baum, K., Uhde, A., Sterz, S., Hirsch, M. C., & Langer, M.
- Affiliations
- Philipps University of Marburg (DE)
- German Research Centre for Artificial Intelligence (DE)
- Ritsumeikan University (JP)
- Saarland University (DE)
- University of Freiburg (DE)
- Corresponding author
- Schlicker, Nadine
- Langer, Markus
- Venue
- Computers in Human Behavior
- Publisher
- Center for Open Science
- Published
- 2022-09-17
- Volume
- 170
- Pages
- 108671-108671
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W4409423957
- Cited by
- 79
- References
- 137
- Citations by year
- 2026: 43 · 2025: 25 · 2024: 7 · 2023: 4
- FWCI
- 74.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computers in Human Behavior (publishedVersion, cc-by)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (TRR 248, 389792660)
- Volkswagen Foundation (AZ 98512, 98514, 98513)
- Bundesministerium für Bildung und Forschung (01IW24007, 16SV8640)
- Daimler und Benz Stiftung (45-06/24)
- European Regional Development Fund
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Keywords
- trustworthy AI, trustworthiness assessment, perceived trustworthiness, trust propensity, trusting behavior, cue detection, AI alignment, AI regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review