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'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Open accessPublished version of a preprint
Abstract
Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.
Checked 2026-10-06 · 5 references not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Three experimental studies found that people apply justice dimensions to algorithmic decisions much as they do to human decisions, but explanation-style effects were mostly visible only when participants compared multiple styles for the same case. Case-based explanations were consistently associated with lower justice perceptions than sensitivity-based explanations, while repeated exposure to a single explanation style largely obscured explanation effects.
Key findings
- Justice constructs correlated similarly to the human decision-making literature: Spearman’s rho was 0.69 for fair process–deserved outcome, 0.60 for fair process–appropriate factors, 0.51 for appropriate factors–deserved outcome, 0.40 for fair process–understanding, 0.40 for appropriate factors–understanding, and 0.36 for understanding–deserved outcome.
- In the between-subjects study (325 participants, 5 conditions of n = 65), explanation styles generally did not significantly affect justice perceptions, except for fair process in loans, F(3, 258) = 2.71, p = 0.046, and appropriate factors, F(3, 258) = 5.35, p = 0.001.
- In the within-subjects study (65 participants), significant explanation effects appeared across all questions and contexts: fair process in loans, F(3, 260) = 7.52, p < .001, and insurance, F(3, 260) = 4.5, p = .004; appropriate factors in loans, F(3, 260) = 3.312, p = .02, and insurance, F(3, 260) = 6.44, p = .0003; deserved outcome in loans, F(3, 260) = 7.31, p = .0001. Case-based explanations produced lower appropriateness, fair process, and loan deservedness than sensitivity-based styles.
- Qualitative themes included lack of human touch/indignity, interpreting the system’s reasoning, acceptability of statistical inference and generalisation, actionability, unaccounted aspects, and the meaning/relevance of moral concepts; some participants saw the algorithmic nature as mitigating justice judgments.
Methods
- Three studies: Phase 1 was an in-person lab study with 19 UK participants, using semi-structured think-aloud interviews over 15 fictional negative-outcome cases across 5 contexts (loan, promotion, car insurance, airline re-routing, bank account freeze), each with one of 4 explanation styles (input influence, sensitivity, case-based, demographic) or a no-explanation control; data were thematically analysed.
- Phase 2 comprised two online Prolific Academic studies with UK participants over 18: a between-subjects study with 325 participants randomly assigned to 5 conditions (n = 65 each) and 12 cases with one explanation style or control, and a within-subjects study with 65 participants using only loan and insurance cases, presenting four different explanation styles for a single case in random order.
- Measures were agreement with five 5-point Likert justice statements (agreement, understanding, appropriateness of factors, fair process, deserved outcome); analysis used Spearman’s rho correlations and ANOVA with Tukey’s post-hoc paired tests.
Limitations
- Stated: samples were not representative—the lab study drew from an affluent, academically dominated UK city, and the online studies were not gender balanced, with almost double the number of females to males; scenarios were hypothetical and did not directly affect participants, lacking first-person consequences and options such as human review or appeal; explanations were not drawn from real machine learning model outputs; differences in explanation text length and format may have confounded results; further confirmatory work is needed on why explanation styles differ.
- Stated/evident: the airline context was excluded from the online studies because lab participants almost universally judged it very unfair and undeserved, preventing useful conclusions; the lab sample was small (19 participants), and online participants were limited to the UK.
- Evident: the between-subjects design repeated a single explanation style, which may have led to habituation, while the within-subjects design made style differences more salient through direct comparison; neither design may fully capture real-world exposure patterns.
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Claims the paper makes
51 Own result · 21 Not checked yet · 17 Own interpretation · 7 Own method · 6 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity.”
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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions.”
hedgedcorrelationaln = 3- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Through iterative testing we found that the use of dialog boxes focused attention on explanations (see Figure 2).”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast to this apparent lack of humanity (or perhaps to counteract it), some participants attempted to humanise or anthropomorphise the system [48], one participant for instance describing it as 'rude' ( OR ).”
What the sources say
- Not checked yetcites [48] 48. Clifford Nass, Jonathan Steuer, Ellen Tauber, and Heidi Reeder. 1993. Anthropomorphism, agency, and ethopoeia: computers as social actors. In INTERACT'93 and CHI'93 Conference Companion on Human Factors in Computing Systems . ACM, 111-112. source
plainSingle case- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In judging the appropriateness of certain features, or the fairness of a decision, issues of personal responsibility and the possibility of acting differently were frequently raised.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some participants were happy to endorse negative decisions if they were able to identify a reasonable alternative course of action available to the participant which would resulted in a different outcome, as in sensitivity -based explanations:”
plainconditional- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that people generally did not pay attention to explanations independent of scenario descriptions.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“An initial reaction from many participants concerned the indignity or 'weirdness' of algorithmic decision-making compared to human decision-making.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We also found that people tended to interpret these justice construct statements as a proxy for the simpler question of whether or not they agreed with the decision;”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, many other participants expressed strong objections to the use of statistical inferences to make judgements about people in itself:”
boosted- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
- It may state two separate claims as one. 25
- Probably not a substantial claim. 47
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles-under repeated exposure of one style, scenario effects obscure any explanation effects.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However in this case, some participants were more comfortable with inductive inferences; AS agreed with the computer's decision to deny a loan applicant with existing debts because they had already 'proven not to be able to pay it back'.”
plainSingle case- Asserted: passes — The authors put it forward as true.
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- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — 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 airline context was excluded at this stage, because experiment 1 had revealed that participants almost universally judged the cases to be very unfair and undeserved, thus preventing any useful conclusions to be drawn from this context.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Where an individual was deemed at fault for risky or otherwise undesirable past behaviour, participant AS believed the decision was particularly justified:”
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One participant argued that a truly random system could also be seen as more 'fair', at least for certain definitions of fairness:”
plainSingle case- Asserted: fails — The authors may not put it forward as true.
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Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the between-subjects study, explanation styles generally did not significantly affect justice perceptions, with the exception of fair process in loans only (loan F(3, 258) = 2.71, p = 0.046) and appropriate factors (F(3, 258) = 5.35, p = 0.001) showing significant between group differences.”
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.
It may state two separate claims as one.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
- Rests on an assumption: Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
- Rests on an assumption: The four explanation styles are sufficiently distinct and were correctly perceived by participants as intended.
- Rests on an assumption: The sample of Prolific Academic participants is similar enough to the lab study participants to allow comparison.
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
“In some cases, what was missing was not just information, but also complexity:”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For some participants, the very idea of an algorithmic system making an important decision on the basis of past data seemed unfair;”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In others, the missing element was a sense of proportionality.”
plainSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whereas for others, delegation of such responsibility to a computer, and the authority of statistical models, actually removed the relevance of questions of fairness altogether; the system 'just does what it is supposed to', and as long as its inferences are accurate, it could be seen as 'statistically fair' ( PC ).”
hedgeddirectSingle case- Asserted: fails — The authors may not put it forward as true.
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Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — 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 meaning and relevance of moral concepts like 'fairness' and 'desert' in the context of a computer system was occasionally questioned by certain participants.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In some cases, while the individual premises seemed understandable, their relevance to the reasoning behind the overall decision was not:”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Issues raised in this regard were the importance of scientific rigour, including the use of sufficient sample sizes:”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many criticisms of the decisions came down to things that were missing or unaccounted for.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — 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 results (see Table 1) partially confirmed our hypotheses, as the stronger correlations were connected to the construct connections we believed would exist.”
plaincorrelationaln = 325- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
- Rests on an assumption: Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition to comparisons between different individuals, participants also commented on the acceptability of judging a single individual's future behaviour on the basis of their past behaviour.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — 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 participant CS was concerned that behaviour change in response to an algorithm could prove dangerous”
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tukey's post-hoc paired tests (see Figure 3) showed that casebased explanations result in lower perceptions of appropriateness, fair process perception, and (in the loans case) deservedness, consistently compared to sensitivity based styles and occasionally compared to other styles.”
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.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
- Rests on an assumption: Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
- Rests on an assumption: The four explanation styles are sufficiently distinct and were correctly perceived by participants as intended.
- Rests on an assumption: The sample of Prolific Academic participants is similar enough to the lab study participants to allow comparison.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Several responses suggested that explanations ought to help guide future action, something which was particularly lacking in the demographic explanations:”
hedged- Asserted: passes — The authors put it forward as true.
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- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Others tried to articulate the system's reasoning as a series of rules and consequences:”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was a significant effect of explanation styles on perceptions of fair process (loan scenario F(3, 260) = 7.52, p < .001; insurance scenario F(3, 260) = 4.5, p = .004), on the perception of appropriate use of factors (loan F(3, 260) = 3.312, p = .02; insurance F(3, 260) = 6.44, p = .0003), and on the perception of deserved outcome (only in the loans case, F(3, 260) = 7.31, p = .0001).”
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.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
- Rests on an assumption: Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
- Rests on an assumption: The four explanation styles are sufficiently distinct and were correctly perceived by participants as intended.
- Rests on an assumption: The sample of Prolific Academic participants is similar enough to the lab study participants to allow comparison.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the same participant, the specificity of the figures used in some explanations (particularly, input influence and sensitivity ) lead her to conclude that the computer must be using arbitrary thresholds, which in itself seemed 'mean':”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Many of the reactions concerned attempts to decipher the logic of the algorithm.”
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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Faced with seemingly inexplicable reasoning processes, some participants concluded that the system must be simply 'making it up' ( OR ) and therefore on the face of it unfair.”
plaindirectSingle case- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Others considered it problematic that with an automated system, 'there's no sense of negotiation' ( DR ), and no opportunity for 'human interaction' ( CS ).”
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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For some, it was the absence of important information:”
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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Participant PC struggled to evaluate the moral dimensions of computer-driven decisions:”
plainSingle case- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A series of comments reflected upon the ways in which the very use of an algorithmic decision-making system could be impersonal and dehumanising for the subject of the decision.”
hedged- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some emphasised that an individual who seems similar to past individuals may in fact have extenuating circumstances, particularly in response to case-based explanations:”
hedged- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — 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 think-aloud responses from the lab study resulted in five major themes: the lack of human touch; interpretations of the system's reasoning; the use of statistical inference; the degree of actionability in an explanation; important aspects which were seen to be unaccounted for by the system; and the meaning and relevance of moral concepts.”
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.
It may state two separate claims as one.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For DR , here was a sense that the very notion of 'fairness' may not be attributable to a system that is primarily designed to serve a different organisational goal like efficiency:”
hedgedSingle case- Asserted: fails — The authors may not 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 authors may not put it forward as true. The paper’s conclusions may not depend on it.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
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
“Conversely, where a judgement was made on the basis of circumstances which were outside the individual's control, or the course of action suggested was deemed unrealistic, the outcome was seen as undeserved:”
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. It may state two separate claims as one.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One participant described how she might have it both ways, simultaneously entertaining dual standards of justice, one juridical, the other computational;”
hedgedSingle case- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
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
“Such findings provide a rationale for giving individuals rights to information about significant decisions; requiring decision-makers to explain their decisions should promote informational justice, which should help people assess procedural justice and distributive justice in turn.”
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.
Probably not a substantial claim. It may state two separate claims as one.
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
“Work on perceptions of justice reveals much about the role of explanations in ethical assessments of human decisions, but the applicability of these findings to algorithmic decisions and the range of proposed explanation systems proposed for them remains largely unexplored.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no 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
“Such remarks suggest that if algorithmic decisions are held to norms of social behaviour, interactional justice may be a relevant dimension for evaluation.”
hedgedconditional- 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. It may state two separate claims as one.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
In the paper
“According to Colquitt and others, justice perceptions can be broken down into several aspects or antecedents [10].”
What the sources say
- Not checked yetcites [10] 10. Jason A Colquitt, Donald E Conlon, Michael J Wesson, Christopher OLH Porter, and K Yee Ng. 2001. Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research. Journal of Applied Psychology 86, 3 (2001). DOI: http://dx.doi.org/10.1037//0021-9010.86.3.425 source
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.
In the paper
“According to the literature on justice perceptions above, we should expect to find correlations between fair process and deserved outcome , understanding and fair process , appropriateness of factors and deserved outcome , and appropriateness of factors and fair process [10].”
What the sources say
- Not checked yetcites [10] 10. Jason A Colquitt, Donald E Conlon, Michael J Wesson, Christopher OLH Porter, and K Yee Ng. 2001. Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research. Journal of Applied Psychology 86, 3 (2001). DOI: http://dx.doi.org/10.1037//0021-9010.86.3.425 source
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.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
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 is an effect primarily observed, like most effects in the quantitative part of our study, in the within subject study design, indicating that the act of comparison in a particular scenario is important for these differences to become apparent.”
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.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
- Rests on an assumption: Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
- Rests on an assumption: The four explanation styles are sufficiently distinct and were correctly perceived by participants as intended.
- Rests on an assumption: The sample of Prolific Academic participants is similar enough to the lab study participants to allow comparison.
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
“Thematic codes were independently developed by three coders, after which a single set was jointly agreed upon. The data were then re-coded using the new set of codes.”
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.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
None of the sources cited for this claim have been checked yet.
In the paper
“In this sense, while algorithmic decision-making implicates dimensions of justice, it may also mitigate them, in so far as people resist imputing morality to a computer system [25, 5].”
What the sources say
- Not checked yetcites [25] 25. Batya Friedman and Peter H Kahn. 1992. Human agency and responsible computing: Implications for computer system design. Journal of Systems and Software 17, 1 (1992), 7-14. source
- Not checked yetcites [5] 5. Joanna J Bryson. 2010. Robots should be slaves. Close Engagements with Artificial Companions: Key social, psychological, ethical and design issues (2010), 63-74.
hedgedconditional- 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.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
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
“to avoid this, an additional statement about agreement (1, agreement ) was also included prior to the others.”
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Much of this research is based on field surveys of individuals with personal experience of being affected by decisions (e.g. [79]), although controlled experiments involving simulated decisionmaking and scenario-based methods have also been used [78].”
What the sources say
- Not checked yetcites [79] 79. Kees Van den Bos, Lynn Van der Velden, and Allan Lind. 2014. On the role of perceived procedural justice in citizens' reactions to government decisions and the handling of conflicts. Utrecht Law Review 10, 4 (2014). DOI: http://dx.doi.org/10.18352/ulr.287
- Not checked yetcites [78] 78. Kees Van den Bos, E Allan Lind, Riël V ermunt, and Henk AM Wilke. 1997. How do I judge my outcome when I do not know the outcome of others? The psychology of the fair process effect. Journal of Personality and Social Psychology 72, 5 (1997), 1034. source
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.
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
“Requiring organisations to explain the logic behind their algorithmic decision-making systems (informational justice) enables affected individuals to assess whether the logic of the system is just (procedural justice), which in turn might moderate their assessments of fairness of the decision outcomes (distributive justice).”
hedgedconditional- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no 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
“This was partly due to the hypothetical nature of the scenarios, and partly due to being too human-specific to apply to a computer-based interaction.”
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
“Similarly, the United States' Equal Credit Opportunity Act (ECOA) requires lenders to provide 'statements of reasons' when applications are denied loans [32].”
What the sources say
- Not checked yetcites [32] 32. David C Hsia. 1978. Credit scoring and the equal credit opportunity act. Hastings LJ 30 (1978), 371.
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 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
“Many of the previously mentioned machine learning model explanation systems have not been subjected to user evaluations”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“We also do not yet know much the extent to which findings on correlations between perceptions of justice from human decision-making contexts hold in the context of algorithmic decisions.”
hedged- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“However the nuances of the different explanation approaches recently developed in interpretable ML have not yet been explored in relation to perceptions of justice.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“These aspects of justice are distinct but correlated [10].”
What the sources say
- Not checked yetcites [10] 10. Jason A Colquitt, Donald E Conlon, Michael J Wesson, Christopher OLH Porter, and K Yee Ng. 2001. Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research. Journal of Applied Psychology 86, 3 (2001). DOI: http://dx.doi.org/10.1037//0021-9010.86.3.425 source
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
“Statement 2 ( understanding ) aimed to capture informational justice [27]; statements 3 and 4 ( appropriateness of factors, fair process ) pertained to procedural justice; and statement 5 ( deserved outcome ) addressed distributive dimensions [11].”
What the sources say
- Not checked yetcites [27] 27. Jerald Greenberg and R Cropanzano. 1993. The social side of fairness: Interpersonal and informational classes of organizational justice. Justice in the workplace: Approaching fairness in human resource management, Lawrence Erlbaum Associates, Hillsdale, NJ (1993).
- Not checked yetcites [11] 11. Jason A Colquitt and Jessica B Rodell. 2015. Measuring justice and fairness. In Oxford Handbook of Justice in the Workplace , Russell Cropanzano and Maureen L Ambrose (Eds.). Oxford University Press, Oxford, 187-202.
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. It may state two separate claims as one.
- Rests on an assumption: The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
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
“One possible explanation is that when people repeatedly see the same explanation style, they become habituated and pay more attention to the features of the cases rather than any specific features of the explanation provided.”
hedgeddirect- 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
“the scenarios considered were hypothetical, not affecting the participants directly, and therefore lacked the first-person consequences and significance of a real world decision”
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
“The introduction of algorithmic systems seems likely to shake up previously stable categories and concepts, requiring adjustment and incorporation into one's moral world-view.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
- Rests on an assumption: The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
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 algorithmic nature of these systems results in an array of novel considerations which are not captured by traditional research on perceptions of justice.”
plaindirect- 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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. 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
“people may realistically face multiple explanations for similar algorithmic decisions when applying for a loan, mortgage or credit”
hedged- 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
“creators of ML explanation systems have not typically designed them with the information needs of those individuals facing significant personal consequences of model outputs in mind”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“When exposed to multiple explanation styles regarding the same case, the differences may become more salient.”
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.
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
“differences in the length and format of the text required to impart an explanation between explanation conditions may have been a confounding factor”
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.
The authors state this as an assumption.
The GDPR automated-decision hurdle ('based solely on automated processing') can be read less restrictively than legal analysis has indicated, so the selected scenarios may fall under GDPR.
“The hurdle that a decision must be 'based solely on automated processing' is here read less restrictively than legal analysis has indicated [81, 20].”
The paper relies on this without support.
Explanations are most likely to be requested in response to a decision with a negative outcome for the individual.
“Because explanations are most likely to be requested in response to a decision which has a negative outcome for the individual, and considerations of procedural justice have been found to be more strongly felt in such cases [4], each scenario ended in a negative outcome for the individual.”
The paper relies on this without support.
The justice constructs developed for human decision-making (informational, procedural, distributive) are applicable to and valid for evaluating algorithmic decision-making contexts.
“The justice constructs used in all three studies were based on those developed in the psychology of justice research, summarised by Colquitt et al [11].”
“The remaining questions pertained to informational, procedural and distributive justice.”
The paper relies on this without support.
Participants' Likert-scale responses to the justice statements accurately measure their perceived justice.
“Participants were asked to rate their agreement with five statements on a 5-point Likert scale”
“Participants were asked to indicate their agreement with the 5 measures described in phase 1 using the same Likert scale.”
The paper relies on this without support.
The fictionalised scenarios used in the lab and online studies are realistic and representative enough to elicit valid responses about algorithmic decision-making.
“The first study consisted of in-person semi-structured interviews focused around a series of fictionalised cases as described above.”
“Cases were presented with a brief description of the individual, the decision outcome, the explanation (except in the control condition) and an illustrative image depicting the context.”
The paper relies on this without support.
The four explanation styles are sufficiently distinct and were correctly perceived by participants as intended.
“Each case was presented with one of the 4 explanation styles described above, or a control condition in which no explanation was provided.”
“Each decision was explained using a different style (in a random order), allowing participants to directly compare multiple styles applied to an individual subject.”
The paper relies on this without support.
The thematic analysis themes identified by the coders accurately represent participants' interpretations and reasoning.
“Thematic analysis was undertaken to identify common themes in participants' interpretation, evaluation and reasoning in response to cases. Thematic codes were independently developed by three coders, after which a single set was jointly agreed upon. The data were then re-coded using the new set of codes.”
The paper relies on this without support.
The sample of Prolific Academic participants is similar enough to the lab study participants to allow comparison.
“Participants were recruited via the study platform Prolific Academic, 4 and filtered to include only individuals over 18 based in the UK, in order to maintain similarity with the participants recruited in phase 1.”
The paper relies on this without support.
There is still much to learn about what people want and need to know about algorithms in order to hold them accountable to justice.
“there is still much to learn about what people want and need to know about algorithms in order to hold them accountable to justice”
The paper relies on this without support.
The explanation styles proposed in the interpretable machine learning literature vary in the information they contain.
“the explanation styles proposed in the interpretable machine learning literature vary in the information they contain”
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
“and that humans would treat decision subjects with dignity and compassion (e.g., Q.31; also in line with previous research [17,68,94]).”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Furthermore, humans”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Binns, Van Kleek, Veale, Lyngs, Zhao, & Shadbolt, 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This philosophically grounded notion is matched by human perceptions that decisions made by algorithms are less trustworthy and fair than those made by humans and that being evaluated by algorithms is dehumanizing ( Binns et al., 2018 ;Lee, 2018 ).”
From The flaws of policies requiring human oversight of government algorithms (2022) · cites this paper as Binns et al., 2018
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“There is evidence that suggests that decision subjects care about justice-related aspects in algorithmic decisionmaking, as they care in human decision-making [17].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [17]
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.
- ConsistentF(3, 258) = 2.71reported p = .046 · recomputed p = .046
“F(3, 258) = 2.71, p = .046”
- ConsistentF(3, 258) = 5.35reported p = .001 · recomputed p = .001
“F(3, 258) = 5.35, p = .001”
- ConsistentF(3, 260) = 4.5reported p = .004 · recomputed p = .004
“F(3, 260) = 4.5, p = .004”
- ConsistentF(3, 260) = 3.312reported p = .02 · recomputed p = .021
“F(3, 260) = 3.312, p = .02”
- ConsistentF(3, 260) = 6.44reported p = .0003 · recomputed p < .001
“F(3, 260) = 6.44, p = .0003”
- ConsistentF(3, 260) = 7.31reported p = .0001 · recomputed p < .001
“F(3, 260) = 7.31, p = .0001”
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 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
§ Do different explanation styles result in differences in perceived levels of justice? · p. 9
“In the within-subjects study-where multiple explanation styles were presented for the same decision-significant effects were observed across all questions and decision-making contexts.”
§ Do different explanation styles result in differences in perceived levels of justice? · p. 9
“on the perception of deserved outcome (only in the loans case, F(3, 260) = 7.31, p = .0001).”
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
§ Quantitative results · p. 8
“we calculated Spearman's rho correlation coefficients (with 0.05 confidence level bootstrapped intervals).”
§ Do different explanation styles result in differences in perceived levels of justice? · p. 9
“Table 1. Spearman's rho correlations for justice constructs with bootstrapped (1000 rep) confidence interval at 0.02 level.”
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
§ Qualitative Results: Themes and reflections · p. 6
“The think-aloud responses from the lab study resulted in five major themes”
§ Qualitative Results: Themes and reflections · p. 6
“the lack of human touch; interpretations of the system's reasoning; the use of statistical inference; the degree of actionability in an explanation; important aspects which were seen to be unaccounted for by the system; and the meaning and relevance of moral concepts.”
Citations
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“A significant body of research in legal and organisational psychology suggests that people do not only care about whether the outcome of a decision benefits them, but also whether it meets standards of justice [71, 40, 11].”
BackgroundJustice at the millennium: A meta-analytic review of 25 years of organizational justice research.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.3.425 · Cited by 5,942 · cited 5×
“According to the literature on justice perceptions above, we should expect to find correlations between fair process and deserved outcome , understanding and fair process , appropriateness of factors and deserved outcome , and appropriateness of factors and fair process [10].”
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“2Although we acknowledge this is an important challenge if explanations are to be more than just comforting stories, as cautioned by Lipton [41].”
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Norman, G. (2010) · Advances in Health Sciences Education · doi:10.1007/s10459-010-9222-y · Cited by 4,850 · cited 1×
“6Some readers might have reservations over parametric tests being applied to ordinal data: we point to recent literature that has emphasised [53] that Likert data are robust to parametric assumptions, and that ‘parametric methods can be utilized without concern for “getting the wrong answer”’.”
BackgroundComparing Individual Means in the Analysis of Variance
Tukey, J. W. (1949) · Biometrics · doi:10.2307/3001913 · Cited by 4,302 · cited 1×
“We used ANOVA tests, followed by Tukey’s post-hoc paired tests [75], to analyse the relative effects of different explanation styles within different study designs.”
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Cited by
Sorted by citations
Items 601–696 of 696
Explainability for machine learning models : from data adaptability to user perception
Delaunay, J. (2023) · theses.fr (ABES) · doi:10.70675/933366c4z576ez464cz9fcfz24627f86dd2f
Bertrand, A. (2024) · theses.fr (ABES) · doi:10.70675/216d3647z78bcz4c68z9ba9z2d0a029cf1b4 · Cited by 1
Reconfiguring Empathy: Affective Outcomes of AI-Mediated Decision-Making
Schmidt, E. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799185
“Emerging work suggests shifts in affective processing, such as increased psychological distance or moral disengagement [35, 38, 62], yet most studies focus on how decision-receivers perceive AI-supported decisions [8, 23], while evidence on decisions-makers remains limited [15].”
Holland, T., Sparrow, L. A., Smith, W., & Webber, S. E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791146 · Cited by 1
What Happened to Scenario-Based Design in HCI?: A Scoping Review
Carroll, J. M., Jo, J., Kim, J., Lin, Y.-F., & Chen, E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790899 · Cited by 2
Cordasco, C. L., & Véliz, C. (2026) · Journal of Business Ethics · doi:10.1007/s10551-026-06305-9
Aljuneidi, S., Heuten, W., Bilyalova, Z., Wolters, M. K., & Boll, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790795 · Cited by 1
Antoniuk, D., & Ivens, B. S. (2026) · Journal of Business-to-Business Marketing · doi:10.1080/1051712x.2026.2657436
Rehearsing the Self with AI: Teens Cross-Domain Use of Chatbots
Sula, C., Dewan, U., & McDonald, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3793144 · Cited by 1
Does Sycophancy Change Decisions? Effect of LLM Sycophancy on AI-Assisted Decision-Making
Li, Z., Pan, J., Liu, Q., Xi, Y., Zhou, Y., Jin, Y., Mao, R., & Chen, P. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790934 · Cited by 5
From AI-driven HRM to engaged employees: the critical role of perceived organizational justice
Abdelal, A. M., Shehata, G. M., & Younis, R. A. A. (2026) · Management & Sustainability An Arab Review · doi:10.1108/msar-12-2025-0521 · Cited by 1
The Ethics of Predictive Analytics in Employment and Hiring Practice
Dordunoo, L. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1016-7.ch001
Bobek, V., Majchrzyk, G., & Horvat, T. (2026) · Diversity and inclusion research · doi:10.1007/978-3-032-12536-1_6
Explainable AI for Employability and Job Retention Prediction Using HR Analytics
Udgata, S., Gundampati, K. S., & Shekhar, B. R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16830-6_34
Evaluating Explainable AI Implementation and User Agency Across Major Social Media Platforms
Alam, S., Pawade, A., Ayub, M. S., Rehman, S. U., & Ayub, A. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11481468
Shymko, V., Бабаджанова, А., & Сафін, О. Д. (2026) · PSYCHOLINGUISTICS · doi:10.31470/2309-1797-2026-39-1-231-264
Probing subjective judgment variance in LLM evaluators: A framework for robust IR evaluation
Kumar, R. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07244-8
“As [ 21] highlighted in earlier algorithmic fairness literature, decision-making systems should be explainable and contestable,something still lacking in current LLMs used for subjective evaluation.”
Suteeca, R., & Singjai, A. (2025) · Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) · doi:10.1109/times-icon67125.2025.11488081
Alqithami, S. (2026) · Mathematical and Computational Applications · doi:10.3390/mca31030069
“In algorithmic decision-making, stakeholders evaluate both distributive and procedural justice, and transparency interventions interact with perceived outcome control [21,22].”
TRuST-M: Evaluating User Trust and Explainability in LLM-Based Web Moderation Systems
Ravi, K., Surale, H. B., & Yuan, J.-S. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3779211.3793172
Post Hoc Fairness Audit of Algorithmic Hiring: A Case Study from the Italian Labor Market
Baytcheva, C., Iulio, A. D., Borghesi, A., & Calegari, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19096-3_21
Neurorehabilitation and AI-Driven Cognitive Therapy
Mavai, A. S., Mishra, D. K., & Sharma, A. (2026) · Cognitive systems monographs · doi:10.1007/978-3-032-18782-6_8
A Human-Centered Privacy (HCP) Approach to AI
Sun, L., Xu, W., & Gao, Z. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_116
Human-Centered Recommender Systems
Hao, S., Ji, L., & Zhang, D. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_119
Principles of Human-Centric Generative AI
Hazra, T., Anjaria, K., Dixit, R., & Funde, N. (2026) · Springer eBooks · doi:10.1007/978-3-032-21412-6_2
AI-Based Early Detection System for Students' Mental Health Using Passive Behavioral Data
Bala, K., Vandna, Sharma, R., Himani, Goindi, S., & Khan, P. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11525537
“Dartmouth College’s Student Life project [ 9-10] tracked students’ movement, phone use, and communication via smartphone sensors to link them to academic success or stress.”
Transformations in Talent Acquisition: Measuring AI’s Effects on Recruitment Efficiency and Bias
CHANDAD, A., Benchekroun, M. A., & Abakouy, M. (2026) · F1000Research · doi:10.12688/f1000research.173468.1
Pogrebna, G., & Hills, T. T. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108939010.035
Behavioural Data Science for Understanding Human Decisions, Choices and Judgement
Pogrebna, G., & Hills, T. T. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108939010.008
Pogrebna, G., & Hills, T. T. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108939010.007
Behavioural Data Science Ethics and Governance Post-AI Act
Motsi-Omoijiade, I. D. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108939010.051
Owoade, A. A., & EGUNJOBI, K. A. (2026) · Federal University Gusau Faculty of Education Journal · doi:10.64348/zije.2026423
“Binns et al. (2018) cautioned against the risk of bias and lack of transparency in AI decision-making, particularly in automated grading and recommendation systems.”
Ethical Employer Branding and the Construction of Employee Psychological Contracts
Vidhya, R., & William Robert., P. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9255-4.ch006
HR Marketing and Human–AI Collaboration
Hirogaki, M., & Rungsithong, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2303-7.ch006
Bias, Fairness, and Accountability in Forensic AI Systems
Jain, A. K., & Vijay, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2278-8.ch005
Rosete, A. T., Leon, G. D. D., Enrique, W. D., Teleb, A., Saludario, R. A., & Cruz, J. D. (2026) · Springer proceedings in business and economics · doi:10.1007/978-981-95-6415-6_61
Algorithmic Justice and Workplace Dignity
Mallick, M. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7966-1.ch006
Nneke, N., Crockett, K. A., & Latham, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7011187
Søraa, R. A., & Ask, K. (2026) · Universitetsforlaget eBooks · doi:10.18261/9788294167234-26-05
Yurdabak, M. K. (2026) · Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi · doi:10.24889/ifede.1788481
Bobek, V., Kokol, B., & Horvat, T. (2026) · Diversity and inclusion research · doi:10.1007/978-3-032-11922-3_14
Karamustafa, E. Y., & Arsan, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5816-1.ch009
Zureigat, Q. M. (2026) · EuroMid Journal of Business and Tech-innovation (EJBTI) · doi:10.51325/d4w0yq25
Harnessing artificial intelligence for revenue generation in South African local government
Hofisi, C., & Sevhake, M. (2026) · Industrial Artificial Intelligence · doi:10.1007/s44244-026-00032-w
“…transformation, local authorities are establishing organizational mechanisms such as public algorithm registries and ethical boards to enhance the level of accountability and openness, showing a broader international progression towards advanced AI applications in local government [ 15 , 16 , 45 ].”
Understanding Awareness of Biases and Mitigation Strategies in AI Applications
Rothe‐Wulf, A., Sekar, S. D., & Marsden, N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30846-7_34
Understanding User Experience in AI-Based Evaluation Systems: A Systematic Literature Review
Park, H., Chung, G., Kim, C., & Kim, H. K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29653-5_21
Cross-Examining The Code: The Necessary Art of the AI Voir Dire
Kasango, H. S. (2026) · AI Law Politics · doi:10.5709/alp-02.01.2026-01
Balcerak, A., & Woźniak, J. (2026) · Contemporary Economics · doi:10.5709/ce.1897-9254.590
Enhancing Human–AI Interaction in Recruitment: Design Guidelines for a Candidate-Centered Experience
Newroly, B., Rau, P. P., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29903-1_19
Singh, J., Dhurandher, S. K., & Woungang, I. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-23304-2_29
Enhancing Sport Coaching Through AI: A Framework for Integrating Advanced Technologies into Sport
Todorovich, J. R. (2026) · Quest · doi:10.1080/00336297.2026.2702354
“For example, if a performance prediction model is trained primarily on male athletes or athletes from specific populations, the recommendations it generates may unintentionally disadvantage female athletes or minority groups (Binns et al., 2018).”
Institutional AI communication: publisher ideology and perceived algorithmic fairness
Chung, U. C., Kim, W., Lee, S. Y., Park, S. I., & Chang, J. (2026) · AI and Ethics · doi:10.1007/s43681-026-01266-1
“Similarly, in domains such as judicial decision making, AI-generated outputs are often perceived as less biased than human judgments [3].”
Guo, M., Agarwal, V., Alghafes, R. A., Papa, G., & Piccolo, L. (2026) · Journal of Knowledge Management · doi:10.1108/jkm-12-2025-1752
Whose Values? Demographic Influences on Perceptions of Conversational AI Value Alignment
Sadek, M., & Mougenot, C. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816265 · Cited by 1
“…collection vary signifcantly by age and culture [63, 72], research on recommender systems and personalisation has identifed how user traits infuence trust and satisfaction [35], and work on algorithmic decision-making has revealed that users’ demographics shape their perceptions of fairness [9].”
Joshi, D., Jain, R., Khatri, S., More, S., Sethi, K. K., & Chourasiya, L. (2025) · IEEE International Conference on Innovate for Humanitarian: Tech Solutions for Global Challenge (ICIH) · doi:10.1109/icih67754.2025.11608883
AI-enhanced trait modulation in high-stakes industries: a conceptual perspective
Kroyer, L., & Lütge, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01197-x
“These insights align with Binns et al. [52 ], who argue that transparency and explainability are critical for mitigating over-reliance on AI systems.”
Sun, J., Islam, M. R., & Wang, Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2703326
Detecting Narcissistic Personality Disorder Traits on Forums: Proof-of-Concept Study
Aladağ, A. E., Özgür, A., Akbaş, N. B., Zahmacıoğlu, O., & Bingol, H. O. (2026) · Journal of Medical Internet Research · doi:10.2196/75799
Shetty, A., & Naveen, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5213-8.ch007
Adoption of AI-Driven Technologies and Its Impact on Employee Commitment
Rao, P. B., Inampudi, P., Purushotham, H., Vidya, M. R., & Kumar, Y. S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5213-8.ch018
Folorunso, J. O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5213-8.ch020
Joshi, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5213-8.ch023
Trust, Transparency, and Ethics in AI-Driven Consumer Engagement
Haque, A., Faisal-E-Alam, M., Akter, M. S., Rupa, R. A., & Islam, M. Z. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2977-0.ch001
Reporting and Discussing Findings
Kovač, N., Ratković, K., Simeunović, M., Farahani, H., & Watson, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-18312-5_24
Gender Bias in Human-Computer Interaction: A Review
Biju, I., & Turcanu, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-21174-3_35
Bridging Technology and Ethics in Dialogue Systems: A Survey of Real-World Challenges
Shah, J., & Thakkar, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27154-9_37
Not All Trust Is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making
Spillner, L., Ringe, R., Porzel, R., & Malaka, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-26717-7_15 · Cited by 3
Wheeler, E., Cohen, L. J., Sobrero, M., Bloch‐Elkouby, S., Lin, N., Hayek, R. E., Levy, Y. A., & GALYNKER, I. I. (2026) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2026.08.007
Özcan, B. A., Demirbaş, A. B., Kalkan, G. E., & Alphan, M. E. (2026) · Studies in big data · doi:10.1007/978-3-032-18562-4_16
69Chapter 5 Agentic AI in Human Resource Management: Redefining Sustainable Workforce Governance
Shabu, K., Harinath, D., & Jithesh, A. (2026) · De Gruyter eBooks · doi:10.1515/9783112237687-005
AI-Powered Mathematical Assistants: Redefining Problem-Solving Paradigms
Nurhasanah, F., Usodo, B., Kuswardi, Y., Chrisnawati, H. E., & Sutopo (2026) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai68849.2026.11649329
Digital Leadership for Human Resource Management and Enhancing Performance in the IT Sector
Bhardwaj, S., Singh, A., Laxmi, J., Vandana, Ashu, & Kumar, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3673-0.ch011
Calibration labour: making careers machine-legible under algorithmic hiring
Zhan, X. (2026) · Information Communication & Society · doi:10.1080/1369118x.2026.2715575
Hohmann, B., Fábián, A., & Szőke, G. L. (2025) · Juridical Tribune - Review of Comparative and International Law · doi:10.62768/tbj/2025/15/1/03 · Cited by 3
Liu, D., & Fan, G. F. (2026) · Social Media + Society · doi:10.1177/20563051261475922
König, P. D. (2026) · The American Review of Public Administration · doi:10.1177/02750740261481775
Agentic AI for Thai Insurance Licence Revocation: A Cognitive Tools and RAG Approach
Kulchotirat, T., Kangkachit, T., & Jitkongchuen, D. (2026) · International Conference on Big Data Analytics and Practices (IBDAP) · doi:10.1109/ibdap69915.2026.11677751
“Empirical work on procedural fairness [18] finds that explanation quality directly influences public acceptance of algorithmic decisions.”
Kim, B., Lee, D., & Kim, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2728939
Cobertura jornalística sobre Inteligência Artificial: um olhar sobre o cenário brasileiro
Essenfelder, R., & Ranieri, P. (2026) · Anagramas - Rumbos y sentidos de la comunicación · doi:10.22395/angr.v25n49a02
“Como destacam Binns et al. (2018), a falta de transparência nos sistemas de IA e a dificuldade de rastrear o uso de dados pessoais ampliam os riscos de discriminação e abuso, exi-gindo mecanismos robustos de governança e responsabilização.”
Human and AI in Decision Making
Arora, A., & Arora, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9599-9.ch015
Tillitsverdig kunstig intelligens i forvaltningen
Henden, E. (2026) · Tidsskrift for samfunnsforskning · doi:10.18261/tfs.67.3.3
Emotional intelligence in tutoring systems: Practical guidelines for enhancing technology acceptance
Schmitz-Hübsch, A., & Wirzberger, M. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101352
AI AND AGEISM: DIGITAL EXCLUSION AND WORKPLACE CHALLENGES FOR OLDER INDIVIDUALS
Tümtürk, T., & ÇEVİK, Ö. C. (2026) · Akademi Sosyal Bilimler Dergisi · doi:10.65083/asbider.1984719
Explanation Needs Emerge Before Explicit Requests in Human-Robot Interaction
Kontogiorgos, D., Gustafson, J., & Shah, J. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831198
“Continuous decision-making settings, such as finance and autonomous driving, predominantly use automatic explanations [7, 12, 14, 15, 63, 67], while human resources and e-commerce often combine automatic and on-demand delivery [10, 17, 22, 60, 81].”
Stratejik Yönetimde Paradigma Değişimi: Yapay Zekânın Yükselişi
Savaşkan, E. (2026) · İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi · doi:10.17336/igusbd.1718992
Ma’rof, A. A., Isa, H. H. M., & Kamaruddin, M. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-4659-3.ch011
Algoritmik Yönetişimde Psikolojik Güvenlik ve Çalışan Sesi
GÜVENER, H. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1487.c5929
Zipperling, D., Deck, L., Lanzl, J., & Kühl, N. (2026) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3858913
The algorithmic third actor in service work
Khan, F. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2744747
User Perceptions of Usage-Based Insurance: Benefits, Challenges, and Impacts on Driving Behavior
Ngereza, A. S., Balyagati, P. F., Kitali, A. E., Kidando, E., & Kutela, B. (2026) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981261491702
Who benefits from LLMs? Prompt engineering competence and equity in higher education
Li, Z., Liu, Y., Jiang, J., Zhang, Z., Li, Z., & Chen, W. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00633-6
“Importantly, perceptions of educational fairness extend beyond technical considerations, encompassing judgments about distributive fairness (who benefits), procedural fairness (how policies are set and enforced), and substantive fairness (whether outcomes become more equal) (Binns et al., 2018).”
Pranav, P., & Ramya, H. P. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0348-0.ch008
Metadata
- Authors
- Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N.
- Affiliations
- University of Oxford (GB)
- University of Oxford, Oxford, Oxfordshire , United Kingdom
- University College London (GB)
- Venue
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-21
- Pages
- 1-14
- OpenAlex
- W4296978576
- Semantic Scholar
- aba609e6c5c9b58f9dac272b75983f75a7f190e5
- Cited by
- 691
- References
- 92
- Citations by year
- 2026: 139 · 2025: 204 · 2024: 87 · 2023: 60 · 2022: 76 · 2021: 53 · 2020: 35 · 2019: 31 · 2018: 6
- FWCI
- 56.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- UK Engineering and Physical Sciences Research Council (EP/J017728/2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- algorithmic decision-making, perceived justice, algorithmic transparency, explanation styles, accountability, arbitrariness, indignity, generalization, automated decision-making, EU data protection law, lending, insurance quotes
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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.
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