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Trust in AI and Its Role in the Acceptance of AI Technologies
Open accessAbstract
As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants’ attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Two studies extend the technology acceptance model (TAM) by adding trust in AI, finding that trust indirectly affects intention to use AI through perceived usefulness and attitude. Study 2 identifies two trust dimensions—human-like trust and functionality trust—with functionality trust showing a greater total impact on usage intention than human-like trust.
Key findings
- In Study 1 (N = 312 college students), the path model explained 52% of variance in usage intention (R² = .52); attitude was the strongest direct predictor of behavioral intention (β = .62, p < .001), and trust had a significant indirect effect through perceived usefulness and attitude. Total effects on usage intention were perceived ease of use β = .63, perceived usefulness β = .46, and trust β = .32 (all p < .001).
- In Study 2 (N = 640 representative U.S. adults), exploratory factor analysis yielded two trust dimensions: human-like trust in AI (six items) and functionality trust in AI (five items). The TAM results replicated: attitude predicted intention (β = .65, p < .001), perceived ease of use had total effects of β = .71 in both models, and perceived usefulness had total effects of β = .57 and β = .56 (all p < .001).
- Both trust dimensions had significant indirect effects on intention to use smart technologies, but functionality-related trust had a greater total impact on usage intention (β = .42, p < .001) than human-like trust (β = .36, p < .001).
Methods
- Study 1: online survey conducted October–December 2020 with 312 undergraduate students from a large Midwest university (56% female; ages 18–29, median 21; 96% had used at least one AI voice assistant). Path analysis was conducted in R using lavaan with maximum likelihood estimation and bootstrapped 95% confidence intervals (k = 1,000).
- Study 2: online survey conducted April 2021 using a national Qualtrics panel with quota sampling for gender, age, and race based on U.S. Census data; 640 respondents participated (50.1% female, mean age = 46.43, SD = 17.83; 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other). Trust items were factor analyzed, yielding two dimensions, and two separate path models tested human-like trust and functionality trust using the same analytic approach as Study 1.
- Measures: perceived ease of use, perceived usefulness, attitude, and behavioral intention were adapted from TAM scales; Study 1 trust in AI voice assistants used four created items; Study 2 trust items were constructed around benevolence/helpfulness, integrity/reliability, and competence/functionality.
Limitations
- Stated in the paper: the studies focused on consumer AI products (voice assistants and smart technologies) associated with lower risk, so findings may not generalize to high-stakes AI such as self-driving cars or healthcare AI; trust is dynamic and may differ across stages such as initial versus ongoing trust; the path models are correlational and cannot guarantee causal effects; and online panel respondents may not be truly representative because they have computer and internet access, skewing toward higher technology acceptance.
- Evident from the text: Study 1 used a convenience sample of college students, limiting generalizability; both studies relied on cross-sectional self-report survey data, so causal direction among trust, perceived usefulness, attitude, and intention cannot be established from these data alone.
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94 Own result · 27 Not checked yet · 18 Own interpretation · 15 Own method · 7 No basis given
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In the paper
“The perceived usefulness of an AI voice assistant also positively influenced users' attitude toward it ( 𝛽 = .42, p < .001)”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The validated model presents a high explanatory power, with 52% of variance explained ( R 2 = .52).”
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“the path model demonstrated a good fit with the data: 𝜒 2 (1) = .32, p = .58; RMSEA = .00 [.00, .12]; SRMR = .01; CFI = 1.00; TLI = 1.01.”
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In the paper
“trust perception ( 𝛽 = .39, p < .001)”
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“Moreover, trust positively predicted perceived usefulness.”
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In the paper
“In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust.”
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In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
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In the paper
“willingness to use it ( 𝛽 = .20, p < .01)”
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In the paper
“A path analysis confirmed that trust had a significant effect on the intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“attitude ( 𝛽 = .26, p < .001)”
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In the paper
“All other measures of fit (CFI above .95, SRMR below .05, and RMSEA below .08) showed that the models were adequate in reproducing covariances among the variables.”
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In the paper
“The models fit the data well: human trust in AI, 𝜒 2 (1) = 4.92, p = .03; RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98; technology-related trust in AI, 𝜒 2 (1) = 4.85, p = .03 RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98.”
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In the paper
“The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The values in Table 2 indicate that the constructs are moderately correlated.”
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In the paper
“Trust also predicted positive attitudes, which in turn influenced usage intention (H4b).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“perceived ease of use had the largest total effect on usage intention ( 𝛽 = .63; p < .001), followed by perceived usefulness ( 𝛽 = .46; p < .001) and trust perception ( 𝛽 = .32; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“In addition, we found that trust can influence and is influenced by the factors included in the TAM.”
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“Trust was associated with increased perceived usefulness, which in turn increased usage intention (H4a).”
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In the paper
“Items from the three constructs were factor analyzed, which yielded a two-factor structure, based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation. The items measuring benevolence and integrity were grouped together, and competence was treated as another dimension.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Perceived ease of use, perceived usefulness, and attitude were positive predictors of usage intention.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The results of indirect effects revealed that all mediated paths depicted were statistically significant, including the hypothesized (H5) paths (see Table 3).”
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In the paper
“Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, trust predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In both path models, participants' intention to use a smart technology was predicated on its perceived ease of use (total effect 𝛽 = .71; p < .001; 𝛽 = .71; p < .001) and perceived usefulness (total effect 𝛽 = .57; p < .001; 𝛽 = .56; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“both trust dimensions ( 𝛽 = .56, p < .001; 𝛽 = .51, p < .001)”
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In the paper
“Additionally, the results indicate that the two dimensions of trust in AI-the human-like trust dimension and the functionality dimension -significantly predict perceived usefulness and positive attitude toward smart technology, which in turn, predict greater usage intention.”
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In the paper
“The two types of trust were associated with perceived usefulness, which predicted greater usage intention (H4a).”
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In the paper
“Finally, trust dimensions predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
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In the paper
“Trust also predicted positive attitudes, which in turn affected usage intention (H4b).”
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“In Study 1 and Study 2, we found that perceived ease of use and perceived usefulness were essential determinants for the use of AI technologies, although perceived ease of use had a consistently greater impact on the acceptance of these technologies.”
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“This dimension was a significant predictor of the acceptance of technology, as was the functionality dimension.”
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“both the human-like and the functionality dimensions of trust had statistically significant indirect effects on the intention to use AI smart technologies, thus supporting H5.”
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In the paper
“Factor 1: Human-like trust in AI”
- Also stated in: § Tables and Figures · plain
“Factor 2: Functionality trust in AI”
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- Also stated in: § Tables and Figures · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This finding suggests that trust in technology is a dynamic concept that is still evolving and variable based on the context and the characteristics of the trusted agent.”
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“Also, current voice assistants are designed to be more human-like than previous attempts, and many individuals are communicating with voice assistants as part of their everyday life in the same way they interact with other humans (Sundar et al., 2017), making trust more relevant.”
What the sources say
- Not checked yetcites Sundar et al., 2017
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“Unlike traditional technologies that rely on user input and the execution of rules programmed by a human, AI implies certain levels of autonomy.”
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“Trust is vital to understanding interpersonal interaction, as it contributes to perceived reliability and integrity.”
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In the paper
“In the current study, building on previous work regarding trust, we focus on the two main dimensions of trust in AI: 1) human-like trust in AI, and 2) functionality trust in AI.”
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In the paper
“trust has been examined as a key factor in the acceptance of emerging technologies (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
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In the paper
“The finding is in line with studies that have tested the impact of these constructs on the adoption of various forms of technology (e.g., Beldad & Hegner, 2018; Choi & Ji, 2015).”
What the sources say
- Not checked yetcites Beldad & Hegner, 2018
- Not checked yetcites Choi & Ji, 2015
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In the paper
“The latter dimension pertains to the competency and expertise of the technological features.”
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In the paper
“The constructs 'perceived ease of use' and 'perceived usefulness' were measured with five items, each adapted from the original TAM scale (Davis, 1989).”
What the sources say
- Not checked yetcites Davis, 1989
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In the paper
“The anthropomorphic nature of voice assistants could make trust more critical.”
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“To ensure the representativeness of participants, we adopted a quota sampling method that set quotas for gender, age, and race based on U.S. Census data.”
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In the paper
“This finding suggests that to motivate individuals to adopt or continue to use an AI voice assistant, they should find the technology easy to use and useful.”
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Past studies have applied interpersonal trust to human technology relationships (Calhoun et al., 2019), especially when the technology has human-like characteristics (Gillath et al., 2021).”
What the sources say
- Not checked yetcites Calhoun et al., 2019
- Not checked yetcites Gillath et al., 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For human-like trust in AI, trust is in the agent, not in the specific actions and operations of the agent (J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites J. D. Lee & See, 2004
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Ninety-six percent of participants reported that they had used at least one type of AI voice assistant.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similar to trust in people and trust in technology, trust in automation is summarized in three categories: 1) performance, 2) process, and 3) purpose.”
What the sources say
- Not checked yetcites Lee and See (2004)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Voice assistants are designed to be more human-like (McLean & Osei-Frimpong, 2019), with their voice feature encouraging users to adopt the same social responses that they habitually use in their interpersonal relationships (Reeves & Nass, 1996).”
What the sources say
- Not checked yetcites McLean & Osei-Frimpong, 2019
- Not checked yetcites Reeves & Nass, 1996
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Behavioral intention to use or continue to use an AI voice assistant was measured with three items from the TAM 2 scale (Venkatesh & Davis, 2000).”
What the sources say
- Not checked yetcites Venkatesh & Davis, 2000
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The former dimension pertains to the social and cultural values of the algorithms and the values and ethics that undergird the design of the AI technology.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“We consider that in the context of AI, both trust in people and trust in technology are relevant, since AI technologies are often depicted as being capable of human qualities, including reasoning and motivations, which can induce high expectations and initial trust (Glikson & Woolley, 2020).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“McKnight et al. (2011) explained that trust in technology is qualitatively different from trust in people.”
What the sources say
- Not checked yetcites McKnight et al. (2011)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The TAM is a theoretical framework for explaining the technology usage behavior that has been validated in different technologies across various populations”
What the sources say
- Not checked yetcites Venkatesh & Davis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 640 respondents (50.1% female, age M = 46.43, SD = 17.83, 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other) participated in the study.”
plainn = 640- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, Glikson and Woolley (2020) organized the literature on trust in AI based on the factors that shaped users' cognitive and emotional trust.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lee & Moray (1992)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, human-like trust may explain emotional trust and emotional attachment to technology.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our findings provide further support for the utility of the TAM in explaining the acceptance of AI technologies and the added contribution of trust within the model.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The distinctive perceptions of human-like trust and functionality trust align with studies that propose multiple dimensions and factors of trust in AI and automation (Glikson & Woolley, 2020; Hoff & Bashir, 2015; J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
- Not checked yetcites Hoff & Bashir, 2015
- Not checked yetcites J. D. Lee & See, 2004
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“As past research has already confirmed, trust in AI can be built through embedding specific principles in the design and governance of AI, as suggested in recent publications using the fairness, accountability, transparency, explainability framework (Shin, 2020b; Shin & Park, 2019).”
What the sources say
- Not checked yetcites Shin, 2020b
- Not checked yetcites Shin & Park, 2019
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Still, an opposite direction of the relationship was also found to be valid in another study (Shin, 2020a).”
What the sources say
- Not checked yetcites Shin, 2020a
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“We proposed trust as an antecedent of perceived usefulness based on the findings from the past TAM literature (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, we focused on AI technology applications in the form of consumer products (i.e., voice assistants and smart technologies), which tend be associated with lower risks.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Hoff and Bashir (2015) suggested three layers of trust in automation (dispositional trust, situational trust, and learned trust) based on their review of trust in AI.”
What the sources say
- Not checked yetcites Hoff & Bashir, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It appears the duality of AI as both a functional and social technology must be considered when designing trustworthy AI.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The cognitive construct involves the rational evaluation of AI while emotional trust is typically influenced by irrational factors.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This impact may be because consumers tend to believe that AI technologies are complicated and difficult to use.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Mayer et al., (1995)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The model explains that when people consider an AI technology to be easy to use, they are more likely to trust the technology and consider it useful.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These findings are consistent with Study 1 and prior literature (Gefen et al., 2003; I.-L. Wu & Chen, 2005).”
What the sources say
- Not checked yetcites Gefen et al., 2003
- Not checked yetcites I.-L. Wu & Chen, 2005
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lankton et al. (2015)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .92 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .89 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .92 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .98 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .81 | N/A”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .90 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .91 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper relies on this without support.
Perception of fairness and justice is associated with human trust in AI.
“Moreover, the perception of fairness and justice will be associated with human trust in AI.”
The paper relies on this without support.
Trust is relevant throughout the lifecycle of a technology.
“Indeed, trust is relevant throughout the lifecycle of a technology.”
The paper relies on this without support.
Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.
“Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.”
The paper relies on this without support.
Trust is a dynamic concept that varies depending on the stage of technology.
“Second, trust is a dynamic concept that varies depending on the stage of technology.”
The paper relies on this without support.
Participants in an online survey are likely to have greater access to and experience with various forms of information technology.
“Participants in an online survey are likely to have greater access to and experience with various forms of information technology.”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Regarding reliance, Choung et al. (2023) demonstrated that trust significantly influences reliance intentions and Kahr et al. (2024) found that trust and reliance on AI advice were significantly higher when the AI model was highly accurate and that trust increased over repeated interactions.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Choung et al., 2023
Experimental — accuracy not yet measuredIn the citing paper
“These findings underscore that trust has a significant impact on perceived ease of use, perceived usefulness, and attitudes toward GenAI.”
From Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI (2025) · cites this paper as Choung et al., 2022
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Not confirmedχ²(1) = 0.32reported p = .58 · recomputed p = .572
“χ 2 (1) = .32, p = .58”
- Consistentχ²(1) = 4.92reported p = .03 · recomputed p = .027
“χ 2 (1) = 4.92, p = .03”
- Consistentχ²(1) = 4.85reported p = .03 · recomputed p = .028
“χ 2 (1) = 4.85, p = .03”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Measures
“based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation.”
§ Tables and Figures
“The extraction method was principal axis factoring with an oblique (promax with Kaiser normalization) rotation.”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“| TRU → PU→ ATT → BI | .03 | .06 | .16 | H4c |”
Reported 0.03 · recomputed 0.101556
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | .08 | .76 | .67 |”
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | -.03 | .87 | .72 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ [Figure 2 About Here]
“Regression estimates indicated that all four proposed predictors of AI voice assistant usage intention have statistically significant direct and indirect effects on the dependent variables.”
§ Discussion
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 5×
“The original TAM proposed perceived usefulness and perceived ease of use to be the two determinants of future usage intention (Davis, 1989).”
BackgroundMethodInfluentiallavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · cited 1×
“The analysis was conducted in R using the lavaan package (Rosseel, 2012).”
MethodA Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · cited 3×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
BackgroundMethodAn 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 · cited 7×
“In interpersonal relationships, this corresponds to the consistency of behaviors associated with adherence to a set of norms (Mayer et al., 1995).”
BackgroundMethodInfluentialArtificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 1×
“…growing fear that our reliance on AI comes at the cost of human agency, and that fear is further stoked by the black-box nature of machine learning algorithms, which generate a response that mimics human response without adequate explanation of the underlying process (Barredo Arrieta et al., 2020).”
BackgroundI.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
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 · cited 1×
“Trust is defined as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another” (Rousseau et al., 1998, p. 395).”
BackgroundTrust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635 · cited 6×
“This study follows the hypotheses tested by other TAMrelated studies that have proposed trust as an antecedent of perceived usefulness and subsequent to perceived ease of use while having a direct effect on attitude (Gefen et al., 2003; Kim, 2012; Wu et al., 2011).”
MethodResultInfluentialTechnology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
Background
Research works printed but not linked: 3
- AI HLEG (2019). Ethics guidelines for trustworthy AI. European Commission. https://data.europa.eu/doi/10.2759/346720 · DOI
- Shin D, 2021, New Media & Society, 1
- Wu J., 2007, Journal of Electronic Commerce Research, 8, 128
Cited by
Sorted by citations
Items 101–200 of 966
Chen, X., Luo, X., & Gao, J. (2024) · Asia Pacific Journal of Tourism Research · doi:10.1080/10941665.2024.2414874 · Cited by 32
“Researchers have suggested that trust in AI and privacy protection positively impact AI-based services in the tourism industry (Choung et al., 2023; Jang, 2023).”
Baron, S. (2025) · Philosophy & Technology · doi:10.1007/s13347-024-00837-6 · Cited by 43
Lim, J. S., Shin, D., Lee, C., Kim, J., & Zhang, J. (2025) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2025.2487679 · Cited by 32
Li, J., Wu, H.-Y., Zhang, R.-X., & Wu, T. (2025) · The International Journal of Human Resource Management · doi:10.1080/09585192.2025.2512555 · Cited by 32
“Instead, they turn to their colleagues for assistance with most work challenges and issues (Choung et al., 2023; J. Kim, Merrill, et al., 2022).”
Yankouskaya, A., Babiker, A., Rizvi, S., Alshakhsi, S., Liebherr, M., & Ali, R. (2025) · ACM Transactions on the Web · doi:10.1145/3765895 · Cited by 32
“Specifically, the willingness to share private life with the LLM measures the extent to which users ascribe a degree of confidentiality and responsiveness to the model, a behaviour consistent with parasocial processes identified in media psychology [90].”
Peng, Z., & Wan, Y. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12374-4 · Cited by 38
“Trust is one of the most critical factors for a user when selecting a service provided by humans or AI (Balakrishnan & Dwivedi, 2021; Cheng et al., 2022b; Choung et al., 2022; Zhang et al., 2023).”
Why should I trust you? Influence of explanation design on consumer behavior in AI-based services
Nizette, F., Hammedi, W., van Riel, A. C. R., & Steils, N. (2024) · Journal of service management · doi:10.1108/josm-05-2024-0223 · Cited by 36
Irgang, L., Sestino, A., Barth, H., & Holmén, M. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123967 · Cited by 40
Wardle, C., Urbani, S., & Wang, E. (2025) · Journal of Medical Internet Research · doi:10.2196/79961 · Cited by 43
It’s better than nothing: The influence of service failures on user reusage intention in AI chatbot
Zhang, J., Lu, X., Zheng, W., & Wang, X. (2024) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2024.101421 · Cited by 31
Sharma, A. K., & Sharma, R. (2024) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-09-2024-2214 · Cited by 40
Liu, Y., Zhang, Z., & Wu, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13403-0 · Cited by 39
“While numerous studies have examined the impact of fear of AI on AI adoption, it is often analyzed as an antecedent or medi - ating variable (Choung et al., 2023; Lukyanenko et al., 2022), with limited research specifically addressing its role in GenAI contexts.”
Trusting emotional support from generative artificial intelligence: a conceptual review
Volpato, R., DeBruine, L. M., & Stumpf, S. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100195 · Cited by 52
“The high complexity of modern technology, difficult for individuals and society at large to understand, makes trust an important influence on choosing to adopt and rely on technologies like e-commerce (Wu et al., 2011), automation (Lee & See, 2004), and AI (Choung et al., 2023).”
Joshi, H. (2025) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2025.100332 · Cited by 41
Chiu, M.-L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13612-7 · Cited by 29
“It involves understanding how users perceive, engage with, and depend on computer-based systems, directly impacting the effectiveness and acceptance of technological interfaces and solutions (Choung et al., 2022; John-Mathews, 2022; Omrani et al., 2022; Sun et al., 2023).”
Salhab, R. A., & Aboushi, M. M. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1640212 · Cited by 55
Lelescu, A., Sava, S., Grosseck, G., & Maliţa, L. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06253-1 · Cited by 43
“In the context of AI, trust extends beyond interpersonal relationships to include human-AI interaction (Glikson & Woolley, 2020) and serves as a mechanism for dealing with uncertainty and complexity (Bach et al., 2022; Choung et al., 2022).”
Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
Theis, S., Jentzsch, S. F., Deligiannaki, F., Berro, C., Raulf, A., & Bruder, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_22 · Cited by 37
Applying social cognition to feedback chatbots: Enhancing trustworthiness through politeness
Brummernhenrich, B., Paulus, C. L., & Jucks, R. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13569 · Cited by 28
Chen, C., & Cui, Z. (2025) · Journal of Medical Internet Research · doi:10.2196/66083 · Cited by 38
“For instance, a recent study by Choung et al [28] directly applies the TAM theory on AI acceptance and teases out trust as a robustly significant factor.”
Alqurni, J. S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1738774 · Cited by 41
“Trust in AI used items drawn from (Choung et al., 2023).”
Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction
Correia, A., Grover, A., Schneider, D., Pimentel, A. P., Chaves, R., de Almeida, M. A., & Fonseca, B. (2023) · Applied Sciences · doi:10.3390/app13042198 · Cited by 29
Alotaibi, S. M. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2492785 · Cited by 41
“These constructs were adapted from existing literature (Alshahrani & Alhassan, 2023; Amoozadeh et al., 2024; Bancoro, 2024; Chan & Hu, 2023; Choung et al., 2023; Daher & Hussein, 2024; Kohnke, 2024; Liu et al., 2020; Liu et al., 2020; Shinners et al., 2022; Singh & Gupta, 2024; Williams & Patel,…”
Guesmi, M., Chatti, M. A., Joarder, S., Ain, Q. U., Alatrash, R., Siepmann, C., & Vahidi, T. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2262797 · Cited by 31
“Research on Artificial Intelligence (AI) underscores the importance of user-friendly AI systems that foster trust, supported by factors like fairness, accountability, transparency, and ethics (FATE) (Choung, David, & Ross, 2023; Vianello, Laine, & Tuomi, 2023; Wahde & Virgolin, 2023).”
Wang, Y., Liu, W., & Yao, M. Z. (2024) · New Media & Society · doi:10.1177/14614448231223517 · Cited by 34
Persist or Abandon? Exploring Chinese Users’ Continuance Intentions Toward AI Painting Tools
Xu, J., Ren, X., Huang, Y., Wu, R., & Pan, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3424946 · Cited by 30
“Choung et al. [85] verified the influence of PT in the continuous use of AI voice assistants by college students, and Nguyen et al. emphasized PT as the strongest influencer of users’ continuance intention to use the bank’s AI chatbot in their study [13].”
BackgroundHui, M., & Li, N. (2024) · IEEE Access · doi:10.1109/access.2024.3441088 · Cited by 29
“Technology trust plays a pivotal role in determining whether users continue to use a particular technology [56].”
BackgroundBalasa, K. A., Dumagay, A. H., Alieto, E. O., & Vallejo, R. G. (2025) · Seminars in Medical Writing and Education · doi:10.56294/mw2025668 · Cited by 30
Mouloudj, K., Aprile, M. C., Bouarar, A. C., Njoku, A. U., Evans, M. A., Oanh, L. V. L., Asanza, D. M., & Mouloudj, S. (2025) · Sustainability · doi:10.3390/su17083717 · Cited by 32
Ngo, T. T. A., Vo, T. T. A., & Phan, M. T. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105789 · Cited by 32
Kolar, N., Milfelner, B., & Pisnik, A. (2024) · Information · doi:10.3390/info15060346 · Cited by 25
Neves, B. B., Omori, M., Petersen, A. R., Vered, M., & Carter, A. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117187 · Cited by 25
Kim, J. S., & Baek, T. H. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2429647 · Cited by 27
“…it directly addresses the dynamics of ongoing commitment to a generative AI chatbot, which has received scant emphasis in the uses and grati fi cations theory (Baek and Kim 2023; Katz, Blumler, and Gure-vitch 1973) and the technology acceptance model (Choung, David, and Ross 2023; Davis 1989).”
Sun, J., Wu, Q., Ma, Z., Zheng, W., & Hu, Y. (2025) · Educational Technology Research and Development · doi:10.1007/s11423-025-10495-w · Cited by 38
“Interestingly, the study found that trust has a slight but significant impact on attitude towards use, similar to the findings of a study by Choung (2022).”
Zhang, G., & Yu, T. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105791 · Cited by 34
Cai, Z., He, H., Huo, W., & Xu, X. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2291616 · Cited by 26
“Most studies have investigated acceptance of medical AI and its antecedents based on the technology acceptance theories, such as the Technology Acceptance Model (Choung et al., 2023; Liu & Tao, 2022; Tao et al., 2023; Uzir et al., 2021; Wang et al., 2022) and the Unified Theory of Acceptance and Use Technology (Baudie…”
Perceived artificial intelligence literacy and employability of university students
Wut, T. M., Chan, E. A.-H., Wong, H. S.-M., & Chan, J. K. Y. (2025) · Education + Training · doi:10.1108/et-06-2024-0272 · Cited by 40
Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments
Sun, Y., Xu, C., & Xu, H. (2024) · Managerial and Decision Economics · doi:10.1002/mde.4361 · Cited by 26
You, Me, and the AI: The Role of Third‐Party Human Teammates for Trust Formation Toward AI Teammates
Erengin, T., Briker, R., & de Jong, S. B. (2025) · Journal of Organizational Behavior · doi:10.1002/job.2857 · Cited by 33
“Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System
Kim, S., Eun, J., Oh, C., & Lee, J. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2307670 · Cited by 29
“This finding is consistent with prior research, which demonstrates that an individual’s experience and trust in AI significantly influence their intent to utilize such technologies (Choung et al., 2023).”
Sarigul, B., Schneider, F. M., & Utz, S. (2024) · International journal of human computer interactions · doi:10.1080/10447318.2024.2375797 · Cited by 22
“However, with advancements in speech recognition and natural language processing, VAs are increasingly being recognized for their capacity to perform more complex tasks (Choung et al., 2022; Fox & Gambino, 2021; Lee et al., 2023; Liu et al., 2023).”
Engineering Digital Systems for Humanity: A Research Roadmap
Autili, M., Sanctis, M. D., Inverardi, P., & Pelliccione, P. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712006 · Cited by 23
Greussing, E., Guenther, L., Baram‐Tsabari, A., Dabran‐Zivan, S., Jonas, E., Klein-Avraham, I., Taddicken, M., Agergaard, T. E., Beets, B., Brossard, D., et al. (2025) · Public Understanding of Science · doi:10.1177/09636625241308493 · Cited by 22
“In navigating the black-box nature of AI, trust emerges as a pivotal factor (Choung et al., 2023; Rheu et al., 2021).”
Factors influencing international students’ adoption of generative artificial intelligence
Ittefaq, M., Zain, A., Arif, R., Taufiq, A., Khan, M. L., & Seo, H. (2025) · Journal of International Students · doi:10.32674/fnwdpn48 · Cited by 29
Aquilino, L., Dio, C. D., Manzi, F., Massaro, D., Bisconti, P., & Marchetti, A. (2025) · Informatics · doi:10.3390/informatics12030070 · Cited by 33
“Eight articles investigated a broader concept of AI system [1,4,57,59,72,77–79].”
InfluentialCognitive Risks of AI: Literacy, Trust, and Critical Thinking
Kulal, A. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2582050 · Cited by 32
Navigating ChatGPT: catalyst or challenge for Indonesian youth in digital entrepreneurship?
Herani, R., & Angela, J. (2024) · Journal of Entrepreneurship in Emerging Economies · doi:10.1108/jeee-05-2024-0181 · Cited by 22
How Learners’ Trust Changes in Generative AI over a Semester of Undergraduate Courses
Song, D. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00446-6 · Cited by 28
“Choung et al. (2023) conducted two studies to identify the role of learners’ trust in their intention to use AI voice assistants.”
Metacognitive sensitivity: The key to calibrating trust and optimal decision making with AI
Lee, D., Pruitt, J., Zhou, T., Du, J., & Odegaard, B. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf133 · Cited by 33
“Incorporating and accepting AI input in decisions is contingent on trust (43).”
Xue, J., Niu, Y., Liang, X., & Yin, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2227834 · Cited by 21
“…investigate the antecedents of the performance of VAs (Jain et al., 2023), subjective well-being (Kang & Shao, 2023), continu-ance usage intention (Choung et al., 2023; Jain et al., 2022; Lavado-Nalvaiz et al., 2022; Lee et al., 2022), user acceptance (Canziani & MacSween, 2021; Cao et al., 2022;…”
Assessing AI receptivity through a persuasion knowledge lens
Watson, J., Valsesia, F., & Segal, S. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101834 · Cited by 21
Alshakhsi, S. A. A., Almourad, M. B., Babiker, A., Al‐Thani, D., Yankouskaya, A., Montag, C., & Ali, R. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2477053 · Cited by 22
Building trust for sustained generative AI travel adoption
Foroughi, B., Vu, H. T. M., & Thaichon, P. (2025) · Tourism Review · doi:10.1108/tr-05-2025-0554 · Cited by 25
Algorithmic gender bias: investigating perceptions of discrimination in automated decision-making
Kim, S., Oh, P., & Lee, J. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2306484 · Cited by 23
“…found that trust in ADM can be a ff ected by beliefs that ADM will result in positive outcomes, such as achieving a speci fi c goal (Calhoun et al. 2019; Lee 2018); and expected traits of the outcomes, such as the accuracy, reliability, and usefulness of algorithms (Choung, David, and Ross 2022).”
Peng, W., & Robinson-Tay, K. (2025) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.03.014 · Cited by 26
Understanding AI guilt: the development, pilot-testing, and validation of an instrument for students
Chan, C. K. Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13629-y · Cited by 21
“Trust in AI is composed of human-like trust, which pertains to the social and cultural values of the algorithms, and functionality trust, which pertains to the competency and expertise of the technological features (Choung et al., 2023).”
Fairness and Trust in AI Decision-Making: The Role of Human Involvement and Outcome Favorability
Choung, H., David, P., Mahmud, H., & Norcutt, S. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2576634 · Cited by 26
“In contrast, Choung et al. (2023) observed that in a hiring context, unfavorable outcomes led participants to evaluate human decision-makers more critically than AI systems, suggesting that algorithms may sometimes be perceived as more objective and fairer.”
Self-citationGenerative and Predictive AI for digital twin systems in manufacturing
Dai, D., Zhao, B., Yu, Z., Franciosa, P., & Ceglarek, D. J. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1655470 · Cited by 27
AI in medical education: the moderating role of the chilling effect and STARA awareness
Wu, M., Huang, X., Jiang, B., Li, Z., Zhang, Y., & Gao, B. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05627-4 · Cited by 26
“Most of the studies have proved that PEOU has a positive effect on consumers’ behavioral intention to adopt technology [44, 45], and PU positively affects individuals’ behavioral intention to adopt technology [6, 46, 47].”
BackgroundHua, X., Hasan, N. A. M., Costa, F. D., & Qiao, W. (2024) · Business Systems Research Journal · doi:10.2478/bsrj-2024-0007 · Cited by 19
“Trust plays a pivotal role in the acceptance of AI technologies, as demonstrated by a study that found trust to significantly affect the intention to use AI voice assistants, with perceived usefulness and attitude towards the technology acting as mediators (Choung et al., 2023).”
Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2177421 · Cited by 19
“Demir et al.’s (2022) paper focuses on human-machine teaming and in particular on how interpersonal coordination dynamics between team members are associated with team performance and shared situation awareness in a simulated urban search and rescue task.”
BackgroundInfluentialObenza, B. N., Caballo, J. H. S., Caangay, R. B. R., Makigod, T. E. C., Almocera, S. M., Bayno, J. L. M., Camposano, J. J. R., Cena, S. J. G., Garcia, J. A. K., Labajo, B. F. M., et al. (2024) · American Journal of Applied Statistics and Economics · doi:10.54536/ajase.v3i1.2510 · Cited by 35
“…(Suh & Ahn, 2022), facilitating condition, performance expectancy, effort expectancy, and behavioral intention to use scales (Chatterjee & Bhattacharjee, 2020), social infl uence scale (Kandoth & Shekhar, 2022), AI awareness scale (Isaac et al ., 2017), and AI trust scale (Choung et al ., 2022).”
MethodUser acceptance of AI voice assistants in Jordan’s telecom industry
Al-kfairy, M., Mustafa, D., Al-Adaileh, A., Zriqat, S., & Sendaba, O. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100521 · Cited by 20
Garcia, M. B. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2499158 · Cited by 26
“This relationship extends to AI-driven systems, wherein trust calibration mechanisms influence the perceived reliability and ethical legitimacy of intelligent agents (Choung et al., 2022).”
Jiang, W., Dong-qin, L., & Liu, C. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326558 · Cited by 26
Role of generative AI-enabled customer relationship management solutions in achieving agility
Kumar, A., & Shankar, A. (2025) · Journal of Business and Industrial Marketing · doi:10.1108/jbim-06-2024-0433 · Cited by 30
Artificial Intelligence Versus Arab Universities: An Enquiry into the Saudi Context
Alenezi, F. Z. (2023) · Scientific Journal of King Faisal University Humanities and Management Sciences · doi:10.37575/h/edu/220038 · Cited by 18
Communicating trust and trustworthiness through scientists’ biographies: Benevolence beliefs
Hautea, S., Besley, J. C., & Choung, H. (2024) · Public Understanding of Science · doi:10.1177/09636625241228733 · Cited by 18
“Benevolence information affecting integrity perceptions is not especially surprising, as benevo-lence and integrity perceptions are often highly correlated (Besley et al., 2021) or even considered facets of a singular construct (Choung et al., 2023).”
Self-citationLiao, C.-H. (2025) · Systems · doi:10.3390/systems13030144 · Cited by 18
Choubey, V. M., Chakraborty, D., & Joshi, Y. (2025) · Asia Pacific Journal of Tourism Research · doi:10.1080/10941665.2025.2486015 · Cited by 18
“The trust positively influences consumers to use AI-based devices Choung et al. (2023).”
Cicero, L., Russo, A., Stefano, G. D., & Zammitti, A. (2025) · BMC Psychology · doi:10.1186/s40359-025-02935-2 · Cited by 19
“The role of other variables such as trust, as a relevant multidimensional variable, in the development of models on the acceptance of AI technologies has been confirmed too (e.g [27, 28, 63].,.”
When generative AI messes up:How politeness and attribution shape user reactions to hallucinations
Kim, H., Seo, S., & Lee, S. W. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102958 · Cited by 17
Building Trust in Artificial Intelligence: A Systematic Review through the Lens of Trust Theory
Regona, M., Yiğitcanlar, T., Hon, C. K. H., & Teo, M. M. (2026) · ACM Computing Surveys · doi:10.1145/3789256 · Cited by 27
Ou, M., Ho, S. S., & Wijaya, S. A. (2025) · Science Communication · doi:10.1177/10755470251315097 · Cited by 16
Papathomas, A., Konteos, G., & Avlogiaris, G. (2025) · Information · doi:10.3390/info16020137 · Cited by 35
Zhao, X., Zhang, S., Sun, S., Nan, D., & Kim, J. H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2474498 · Cited by 16
“These relationships have been concluded by several empirical research with respect to game consoles, AI assistants, generative AI, and others (Balakrishnan et al., 2024; Choung et al., 2023; Nan et al., 2022; Saif et al., 2024).”
Ho, S. S., Wijaya, S. A., & Ou, M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2490703 · Cited by 16
“Drawing on this review, Choung et al. (2023) found that emotional trust in AI emerges from the AI’s perceived benevolence and integrity, while cognitive or functionality trust emerges from perceptions of the AI’s competence.”
Choi, S., Jang, Y., & Kim, H. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2194927 · Cited by 16
“Additionally, because of the unpredictable and uncertain nature of AI during its decision-making, which may induce users to view it with suspicion, studies have investigated the role of trust as an indicator for explaining users ’ acceptance of AI (Choung et al., 2022).”
Citizens’ trust and digital attitudes: evidence from city digital transformation in Shanghai, China
Yang, Z. (2023) · Public Administration and Policy · doi:10.1108/pap-01-2023-0014 · Cited by 20
Lassiter, T. B., & Fleischmann, K. R. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686963 · Cited by 17
“[42, p.1730].”
Dogru, E. Ö., & Krämer, N. C. (2025) · Journal of Decision Systems · doi:10.1080/12460125.2025.2593251 · Cited by 15
Shibuya, Y., Nakazato, T., & Takagi, S. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103485 · Cited by 17
Xiao, Y., Xiao-hua, L., & Yao, Y. (2025) · System · doi:10.1016/j.system.2025.103790 · Cited by 20
Noerman, T., Riyadi, R., Yuliaji, E. S., & Natasha, C. A. M. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2542422 · Cited by 15
“…found that perceived usefulness (Pus) and ease of use strongly influence the decisions of ai technology adoption (chatterjee et al., 2021; choung et al., 2023; Davis, 1989). nevertheless, this study extended the model by including other dimensions, such as hedonic motivation which…”
Yang, H., Xu, N., Lin, X., & Zhang, W. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100833 · Cited by 22
Responsibility and AI: Exploring technology acceptance models
Fel, S., Kozak, J., & Horodyski, P. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100852 · Cited by 24
“The effects of AI extend beyond purely technical domains to include social processes, ethical norms, and political decisions (Choung et al., 2023; Dwivedi et al., 2021).”
Arya, V. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104693 · Cited by 16
Human-AI attachment: how humans develop intimate relationships with AI
Shu, C., Lai, K., & He, L. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1723503 · Cited by 18
How Being Outvoted by AI Teammates Impacts Human-AI Collaboration
Hu, M., Zhang, G., Chong, L., Cagan, J., & Goucher-Lambert, K. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2345980 · Cited by 18
“Humans are likely to develop inappropriate trust in AI that can lead to erroneous acceptance or rejection of AI suggestions 1 and hurt human-AI team performance (Choung et al., 2023; Onnasch et al., 2014; Zhang et al., 2021).”
Students’ Response to ChatGPT: An Adaptive Technology-To-Performance Model
Zhang, X., Jiang, H., Qiao, Z., & Li, P. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2386546 · Cited by 17
“24 Recently, TAM was further validated in AI acceptance 25 and consumers’ intention to share rides.”
Kim, S. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103387 · Cited by 22
“…studied as cognitive indicators of algorithm appreciation and aversion, including perceived fairness, trust in algorithms and AI, and emotional reactions (Angerschmid et al., 2022; Araujo et al., 2020; Choung, David, et al., 2023; Lee, 2018; Lee & Baykal, 2017; Lee & Rich, 2021; Wang et al., 2020).”
BackgroundAI in Companies' Production Processes
Maldonado-Canca, L.-A., Cabrera-Sánchez, J.-P., Molina, A. M. C., & González, G. J. B. (2025) · Journal of Global Information Management · doi:10.4018/jgim.366653 · Cited by 19
Shi, J. J., Xia, X., Zhuang, H., Li, Z., & Xu, K. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118059 · Cited by 17
User acceptance of AI-powered training: extending the technology acceptance model (TAM)
Alshammari, M. M., & Al‐Mamary, Y. H. (2025) · Future Business Journal · doi:10.1186/s43093-025-00665-w · Cited by 20
“In the context of the present study, trust in AI implies the user’s confidence in the reliability, competence, and ethical conduct of AI-powered technologies [19].”
Human-AI Interaction and AI Avatars
Liu, Y., & Siau, K. L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48057-7_8 · Cited by 16
Impact of process and outcome quality on intention for continued use of voice assistants
Saavedra, Á., Chocarro, R., Cortiñas, M., & Rubio, N. (2023) · Spanish Journal of Marketing - ESIC · doi:10.1108/sjme-12-2022-0251 · Cited by 14
“Trust is a crucial predictor in technology adoption (Choung et al. , 2022), and positive attitudes toward VAs result in higher trust (Zhang et al. , 2021).”
BackgroundKelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, Ó. (2023) · Ergonomics · doi:10.1080/00140139.2023.2289864 · Cited by 19
“Two items measured trust, ‘I trust AI to make predictions, recommendations, or decisions influencing real or virtual environments’ and ‘AI is a trustworthy channel for me to share my personal details’ (Choung, David, and Ross 2022; Dinev and Hart 2006).”
BackgroundSamman, A. A. A., & Mohamed, A. (2024) · 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis61505.2024.10459667 · Cited by 12
Social Media Use and Attitudes toward AI: The Mediating Roles of Perceived AI Fairness and Threat
Li, W., & Zheng, X. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/3448083 · Cited by 25
Voice-based personal assistant (VPA) trust: Investigating competence and integrity
Huang, R., Kim, M., & Lennon, S. J. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100140 · Cited by 17
Metadata
- Authors
- Choung, H., David, P., & Ross, A.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Choung, Hyesun
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2022-04-20
- Volume
- 39
- Issue
- 9
- Pages
- 1727-1739
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4221014470
- Semantic Scholar
- f281ad6573ed8ea7fb3116a3747b0e80ef522ea9
- Cited by
- 963
- References
- 62
- Citations by year
- 2026: 402 · 2025: 360 · 2024: 142 · 2023: 54 · 2022: 5
- FWCI
- 117.34
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- trust in AI, technology acceptance, intention to use, voice assistants, perceived usefulness, perceived ease of use, attitudes toward technology, trustworthy AI, technology acceptance model
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.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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