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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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BackgroundComparing Individual Means in the Analysis of Variance
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“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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Items 301–400 of 696
Smart, A., James, L., Hutchinson, B., Wu, S., & Vallor, S. (2020) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3375627.3375866 · Cited by 8
“Empirically, Binns [5] found that people do consider justice-related aspects of algorithmic decision-making systems, much as they do for manual decision-making processes, providing evidence for the requirement of defeasibility.”
BackgroundSAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development
Curto, G., & Comim, F. (2023) · Science and Engineering Ethics · doi:10.1007/s11948-023-00448-y · Cited by 10
“In fact, it is argued that when a system is explainable and interpretable it is inherently fairer, since it allows stakeholders take informed decisions on whether to use the ML system (Binns et al. 2018 ).”
BackgroundHarnessing AI for Sustainability
Pancholi, K., & Shukla, P. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6011-8.ch005 · Cited by 7
How Dynamic vs. Static Presentation Shapes User Perception and Emotional Connection to Text-Based AI
Bhat, M. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712131 · Cited by 8
Exploring the Impact of Explainability in Large Language Model (LLM) Applications on User Experience
Wang, Y., Fang, X., Xu, Z., Li, J., & Wang, L. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719941 · Cited by 9
Yurrita, M., Verma, H., Balayn, A., Gadiraju, U., Pont, S. C., & Bozzon, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713145 · Cited by 11
Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers
Armstrong, L., & Metaxa, D. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711038 · Cited by 12
Organizational Impact of AI-Driven Recruitment Practices: A Mixed‑Methods Study
Kulal, A., Dinesh, S., & Abhishek, N. (2025) · Journal of Computer Information Systems · doi:10.1080/08874417.2025.2508860 · Cited by 12
Zipperling, D., Deck, L., Lanzl, J., & Kühl, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732084 · Cited by 9
“Additionally, recent research indicates a link between explanations and fairness perceptions [10, 51, 74, 76].”
InfluentialEslami, M., Fox, S., Shen, H., Fan, B., Lin, Y., Farzan, R., & Schwanke, B. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732173 · Cited by 10
Skouloudis, A., & Venkatraman, A. (2025) · AI · doi:10.3390/ai6080169 · Cited by 10
LLM content moderation and user satisfaction: evidence from response refusals in Chatbot Arena
Pasch, S. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2565668 · Cited by 17
Mitigating Bias in AI-Driven Recruitment : The Role of Explainable Machine Learning (XAI)
Magham, R. K. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241051037 · Cited by 9
Facing Many Objectives for Fairness in Machine Learning
Villar, D., & Casillas, J. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-85347-1_27 · Cited by 7
Kang, B., & Wu, S. (2023) · Journal of Experimental Criminology · doi:10.1007/s11292-022-09512-2 · Cited by 6
“Additionally, given the controversy around algorithmic decision-making (e.g., Binns et al., 2018; Hamilton, 2020; Scurich & Krauss, 2020), it is hard to imagine that the discretion of human decision-makers would be completely removed.”
Algorithms, Creditworthiness, and Lending Decisions
Leal, A. A. (2024) · Data science, machine intelligence, and law · doi:10.1007/978-3-031-47946-5_17 · Cited by 7
Cleared for Takeoff - Artificial Intelligence Acceptance in Air Traffic Control
Schon, J., Bruder, C., Liebherr, M., & Theis, S. (2025) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3729176.3729185 · Cited by 7
“This relationship emerged during model modification based on modification indices and was added because it was theoretically justifiable [14, 82].”
Frimpong, V. (2025) · Administrative Sciences · doi:10.3390/admsci15070263 · Cited by 9
Schäfer, U., Sipos, L., & Müller-Birn, C. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757451 · Cited by 10
From Transparency to Trust: A Literature Review on Explainable AI in Educational Systems
Tınmaz, H. (2025) · Mersin Üniversitesi Eğitim Fakültesi Dergisi · doi:10.17860/mersinefd.1739347 · Cited by 6
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“As Binns et al. [4] note, algorithmic evaluations often reduce human performance to quantifiable metrics, potentially overlooking the qualitative aspects of academic writing.”
Loefflad, C., & Großklags, J. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658986 · Cited by 9
“The impact on judgments of fairness or justice when applying ADM systems to high-stakes decisions might further depend on the specific application scenario [8, 10].”
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Fairness and Bias in Machine Learning Models for Credit Decisions
Andrae, S. (2025) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-8186-1.ch001 · Cited by 8
Justice in the Age of Algorithms
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The Use of Artificial Intelligence in Physical Education and Movement Development in Children
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Privacy, Trust and Ethical Issues
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“Binns et al. [8] argue that the knowledge that a "machine/algorithm" is making important decisions or evaluations of human behavior triggers negative feelings on a user’s side.”
Paçacı, G., Johnson, D., McKeever, S., & Hamfelt, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22750-0_27 · Cited by 6
GDPR Impacts and Opportunities for Computer-Aided Diagnosis Guidelines and Legal Perspectives
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“However, potential clients of such services are concerned on the perception of justice in automated-decision making algorithms [3], as also the possible privacy issues under the flag of public health interest [4].”
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Impact of Chatbots on User Experience and Data Quality on Citizen Science Platforms
Kessel, A.-L., Sahri, S., Groppe, S., Groppe, J., Khorashadizadeh, H., Pignal, M., Pimparé, E. P., & Vignes‐Lebbe, R. (2025) · Computers · doi:10.3390/computers14010021 · Cited by 7
AI Agency in Fact-Checking: Role-Based Machine Heuristics and Publics’ Conspiratorial Orientation
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“For example, PMH has been shown to mediate trust in automated decision‐making, where users may accept machine‐generated outcomes without critical scrutiny (Binns et al., 2018).”
Shifting tides: How public perceptions of GPT regulation evolved before and after GPT-4 on Quora
Xi, Y., & Ngan, L. H. M. (2025) · Telecommunications Policy · doi:10.1016/j.telpol.2025.103001 · Cited by 5
Mental Health Support Using Gen-AI Shot Prompting Technique and Vector Embeddings
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“HITL operationalizes this legal principle, ensuring that automated outcomes are not final and that humans remain accountable for sensitive determinations [35].”
Lin, Z., Dou, H., & Lin, S. (2025) · Technovation · doi:10.1016/j.technovation.2025.103315 · Cited by 10
Human–AI collaboration for marketing capabilities: a meta-analysis
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“This finding aligns well with existing literature, which generally supports the idea that AI assistance can effectively reduce human errors in complex decision-making environments, although genuine synergy remains elusive (Binns et al., 2018).”
Is there consistency in ethical sensitivity in artificial intelligence? A review of language models
Tutar, H., Bilan, S., & Şentürk, Ü. (2025) · Human Technology · doi:10.14254/1795-6889.2025.21-2.4 · Cited by 5
“Secondly, Multisystem Decision Making Theory analyzes the internal consistency of decisions and their relationship to the external context based on human-like cognitive modeling (Binns, 2018; Kahneman, 2011; Srivastava et al., 2022).”
InfluentialExploring the impact of explainable AI and cognitive capabilities on users’ decisions
Cau, F. M., & Spano, L. D. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09438-0 · Cited by 16
“Most of these studies focused on example-based and feature-based explanations (Binns et al. 2018; Lai and Tan 2019; Cai et al. 2019a; Zhang et al. 2020; Bove et al. 2022; Ford and Keane 2023; Chen et al. 2023; Lai et al. 2023b), with a limited number of studies also assessing the effects of…”
A cross-lingual syntactic investigation of gender bias and stereotyping in GPT-4o: English vs Hindi
Arora, S., & Srivastava, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00565-9 · Cited by 6
“This bias encompasses more than just gender; it extends to other social categories like religion, race, nationality, disability, and occupations [1, 29, 33, 37, 38, 42].”
Human-in-the-loop: Explainable or accurate artificial intelligence by exploiting human bias?
Valtonen, L., & Mäkinen, S. (2022) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc-iamot55089.2022.10033225 · Cited by 8
Do Crowdsourced Fairness Preferences Correlate with Risk Perceptions?
Haider, C. M. R., Clifton, C. W., & Yin, M. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645209 · Cited by 8
“We note that existing literature on perceived fairness often limits their investigations to a few widely-discussed real-world scenarios such as recidivism prediction [19, 20, 24], college admission [43], loan repayment [6, 46] and child maltreatment prediction [9].”
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Pinillos, A. S., & Casacuberta, D. (2019) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-030-32722-4_19 · Cited by 5
Schuessler, M., & Weiß, P. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3312823 · Cited by 5
“More recent work from our community includes work by Binns et al. [3] studying how different presentation styles of explanation influence justice perception or work by Rader et al. [10] studying how explanations of the Facebook news feed algorithm influence the beliefs and judgments.”
MethodSmart Technologies and Our Sense of Self: Going Beyond Epistemic Counter-ProfilingPreprint
Delacroix, S., & Veale, M. (2019) · doi:10.31235/osf.io/v2kg9 · Cited by 6
Self-citationSeeds of Sovereignty: Designing Regenerative AI for Plural EpistemologiesPreprint
Kadel, A. (2025) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/f9e65_v1 · Cited by 6
Lay Perceptions of Algorithmic Discrimination in the Context of Systemic Injustice
Lima, G., Grgić-Hlača, N., Langer, M., & Zou, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713536 · Cited by 5
“Prior work suggests that explanations affect people’s mental models of algorithms [85] and their judgments of algorithmic decisions with respect to fairness, trust, and other factors [11, 38, 46, 73, 97, 100, 132].”
Talent Analytics for Workforce Forecasting, Fostering Employee Engagement, and Turnover Prediction
Sanjay, K., & Khalsa, S. K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1942-1.ch017 · Cited by 4
Emotion AI in Job Interviews: Injustice, Emotional Labor, Identity, and Privacy
Ingber, A. S., & Andalibi, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732002 · Cited by 5
“Concerns around injustice in AI and emotion AI use in hiring [8, 11, 81] necessitate this study’s use of an organizational justice lens.”
Explainable AI for Daily Scenarios from End-Users’ Perspective: Non-Use, Concerns, and Ideal Design
Wang, L., Anyi, C. L., Xu, K., Liu, Y., Arriaga, R. I., & Goel, A. K. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735796 · Cited by 7
“Both previous research [7, 14, 108] and our study suggest that a static model cannot meet these varying demands.”
Humanizing AI in Service Workplaces: Exploring Supervisor Support as a Moderator in HPWSs
Atoyebi, T. A., & Sopuru, J. C. (2025) · Sustainability · doi:10.3390/su17177892 · Cited by 9
Building Trust in AI-Powered Assessment Through Explainable Machine Learning Models
Ayanwale, M. A. (2025) · ACM Global Computing Education Conference (CompEd) · doi:10.1145/3736251.3747313 · Cited by 4
Aoyagui, P. A., Stemmler, K., Ferguson, S. A., Kim, Y., & Kuzminykh, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713248 · Cited by 11
“Conventional approaches to Human-AI collaborative decision-making have predominantly focused on decision tasks that have one ultimately correct outcome [74], i.e., ground truth, (e.g., predictions for medical diagnosis [16] or financial assessment [13]).”
How Do Users Perceive Recommender Systems’ Objectives?
Dokoupil, P., Boratto, L., & Peška, L. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748066 · Cited by 6
Responsible AI for AI Sustainable Future: Governance, Ethics, and The Reality Behind the Promise
Atianashie, M. A., Kuffour, M. K., Kyiewu, B., & Serwaa, P. (2025) · Journal of Information Technology, Cybersecurity, and Artificial Intelligence · doi:10.70715/jitcai.2025.v2.i2.012 · Cited by 7
Identifying Algorithmic Decision Subjects' Needs for Meaningful Contestability
Yurrita, M., Verma, H., Balayn, A., Alfrink, K., Gadiraju, U., & Bozzon, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757415 · Cited by 11
“We used positive (+) signs to indicate that a data feature contributed to high fraud risk [16, 31].”
AI said, She said - How Users Perceive Consumer Scoring in Practice
Recki, L., Esau, M., Lawo, D., & Stevens, G. (2023) · Mensch und Computer (MuC) · doi:10.1145/3603555.3603562 · Cited by 4
“As stressed by Binns et al. [7], different styles of explanation lead to significant differences in the perception of justice.”
BackgroundResultInfluentialNavigating Educational Leadership Research With Generative Artificial Intelligence
LaFrance, J. A. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-6220-4.ch006 · Cited by 4
Exploring Social Media's Role in Counseling
Thandayuthapani, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8392-6.ch005 · Cited by 5
Arlinghaus, C. S., Straßmann, C., & Dix, A. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100173 · Cited by 5
Daylamani-Zad, D., & Angelides, M. C. (2020) · IEEE Transactions on Games · doi:10.1109/tg.2020.2989636 · Cited by 5
“The difference between human behaviour and the perception of human behaviour has been an ongoing discussion in various communities [46], [47], [48].”
BackgroundTowards Accountability: The Articulation and Formalization of Fairness in Machine LearningPreprint
Naudts, L. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3298847 · Cited by 4
Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Lyons, H., Velloso, E., & Miller, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449180 · Cited by 4
“Yet, the use of algorithmic systems in decisions that significantly impact lives has also raised concerns relating to fairness and justice [10, 21, 28], human dignity [32, 33], and autonomy [32, 33].”
BackgroundTime Can Invalidate Algorithmic Recourse
Toni, G. D., Teso, S., Lepri, B., & Passerini, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732008 · Cited by 13
“Over the past years, concerns have risen regarding the safety and fairness of these systems [10, 30], as well as their impact on human dignity and autonomy [39, 40].”
The Impact of Social Media Activities on Marine and Coastal Litter in Cyprus
Emirzade, K., & Hüseyinoğlu, M. F. (2025) · Sustainability · doi:10.3390/su17114821 · Cited by 3
“To mitigate these risks, scholars emphasize the importance of transparent, ethically guided AI systems and the inclusion of human oversight in algorithmic content curation [23].”
InfluentialRethinking the judicial duty to state reasons in the age of automation?
Hendrickx, V. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2025.11 · Cited by 8
Towards Ensuring Responsible AI for Medical Device Certification
Mallardi, G., Quaranta, L., Calefato, F., & Lanubile, F. (2025) · doi:10.1109/raie66699.2025.00009 · Cited by 3
“While various governments, research institutions, and companies have promoted Responsible AI guidelines emphasizing unbiased, transparent, and inclusive systems that respect individual rights and societal norms [9], these guidelines often remain abstract.”
Explain to Whom? From User Feedback to Actionable Explanations
Alotaibi, H., & Singh, R. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2510824 · Cited by 3
“You will rate each metric on a scale of 0 (completely disagree) and 10 (completely agree) , inspired by a similar study Binns et al. (2018).”
InfluentialEthical Considerations in the Use of Artificial Intelligence (AI) Among University Students
Triyanto, T., & Handayani, R. D. (2025) · The New Educational Review · doi:10.15804/tner.2025.80.2.07 · Cited by 8
Customers’ perceptions of AI designers: the role of cognitive heuristics and customization
Molina, M. D., Thorson, E., Huddleston, P., & de Bruin, J. (2025) · Journal of Consumer Marketing · doi:10.1108/jcm-02-2025-7595 · Cited by 4
Bao, Q., & Zhu, Q. (2025) · Scientific Reports · doi:10.1038/s41598-025-23510-0 · Cited by 3
“This concern is particularly salient in gender-related discourse, where algorithmic models tend to overemphasize emotional and gendered expressions 44,45 .”
Start Using Justifications When Explaining AI Systems to Decision Subjects
Kolářová, K., & Schmude, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-11108-1_14 · Cited by 5
Kim, Y., & Lee, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2576640 · Cited by 3
Hu, P., Wang, L., Chang, D., Zhu, Z., Chen, Y., Ren, Z., & Dumayi, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2605181 · Cited by 3
“This perspective aligns with literature suggesting that user perceptions often depend on AI’s ability to convincingly simulate autonomy (Binns et al., 2018).”
Algı Yönetimi ve Yapay Zekâ: Medya Manipülasyonunda Yeni Dönem
Çelik, N. (2026) · Iğdır üniversitesi sosyal bilimler dergisi · doi:10.54600/igdirsosbilder.1637527 · Cited by 3
Gawlik, A., Szeliga-Duchnowska, A., & Kaźmierczak, D. (2026) · Quality & Quantity · doi:10.1007/s11135-025-02565-1 · Cited by 4
“AI solutions must be designed and implemented with transparency, fairness, and account - ability, incorporating mechanisms for algorithmic oversight, privacy protection, and bias mitigation (Pew Research Center 2018; Binns et al. 2018).”
Marlowe, T. J., Ku, C. S., Laracy, J. R., Kirova, V. D., & Herbert, K. G. (2026) · Journal of Integrated Design & Process Science · doi:10.1177/10920617251405471 · Cited by 11
“Additionally, the use of AI-generated content raises questions about intellectual property rights, particularly regarding the ownership and attribution of materials produced by AI (Binns et al., 2018; Holmes et al., 2019).”
Ragone, G., Buono, P., Good, J., & Lanzilotti, R. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790765 · Cited by 5
Erlei, A., Cau, F. M., Georgiev, R., Kumar, S. C., Bizer, K., & Gadiraju, U. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791420 · Cited by 7
“For image data, we opted for the ISIC 2018 challenge dataset 9 given its suitability for AI-assisted decisions as in previous work [12, 28, 32, 139, 140, 145].”
Wood, N. G., Robbins, S., Berodt, E. Z., von Westerholt, A. G., Behrndt, M., Budig, H., & Schreiber, D. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01114-4 · Cited by 3
Jo, J., Xie, J., & Carroll, J. M. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609541 · Cited by 3
“While individuals may not always get their desired outcomes, they can have some satisfaction with such outcomes if provided with explanations that guide future action towards potentially different outcomes or if they can appeal a decision [4, 45], another mechanism for contestability.”
BackgroundPellegrino, A., & Stasi, A. (2024) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/15143 · Cited by 9
Rehman, U., Shah, M. U., Iqbal, F., & Fatima, R. (2024) · AI and Ethics · doi:10.1007/s43681-024-00605-4 · Cited by 2
“However, it is essential to acknowledge the limitations of AI systems, such as their potential to be constrained by their programming and training data and their potential to provide explanations that lack contextual awareness and empathy compared to human decision-makers [7].”
Patias, I., Miteva, D., Peltekova, E., Wright, M., & Gasteiger‐Klicpera, B. (2024) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas64331.2024.10845582 · Cited by 3
Future of Productivity Management
Khan, H., Chetan, V., Hukkeri, S. V., & Chaudhary, M. N. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7026-1.ch004 · Cited by 3
Psychological Impact of AI Moderation
Subrahmanyam, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0335-2.ch008 · Cited by 3
Too smart to matter? The psychological meaning of cognitive labor in consumer resistance to AI
Sreejesh, S., Shamim, I., & Krishnan, O. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104412 · Cited by 3
When utility meets ethics: A stakeholder perspective on agentic information systems delegation
Saeed, K., & Prybutok, V. R. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102976 · Cited by 5
Communicating Ethical Considerations in Generative AI Systems
Comes, L., Silveira, J. C. S., Martins, H. C., Lana, C. A., & Villela, M. L. B. (2025) · Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais (IHC) · doi:10.5753/ihc.2025.10939 · Cited by 3
“These fndings align with prior studies that identifed gaps between high-level ethical principles and their implementation in interfaces [Binns et al. 2018, Morley et al. 2020, Shneiderman 2020].”
Hellwig, P., & Maier, G. W. (2023) · Springer eBooks · doi:10.1007/978-3-031-26104-6_4 · Cited by 4
Currie, M. E., & Podoletz, L. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594066 · Cited by 5
“Interviews have also been part of experimental studies probing how people feel towards a range of commercial applications using ADMS, focusing on their concerns related to perceptions of justice [7] [41].”
BackgroundSocial Media Harms as a Trilemma: Asymmetry, Algorithms, and Audacious Design Choices
Cheong, M. (2022) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas55053.2022.10227099 · Cited by 3
“com/teaching/academic/research-academic 4This also includes AI fairness, accountability, and transparency [18]; legal studies [19]; philosophy [20]; and human-computer interaction [21], amongst others — which all seek to untangle the societal and human impacts of implemented algorithmic systems.”
BackgroundTejasvi, K., Vinya, V. L., Padmaja, J. N., Begum, R., & Jabbar, M. A. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55615-9_2 · Cited by 4
Anik, A. I., & Bunt, A. (2024) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc60511.2024.00024 · Cited by 4
“Like other prior work involving AI explainability [19], [27], [33], [109], [110], [111], [112], [113], [114], we used a scenario-based study method.”
BackgroundMethodInfluentialEmotion, Language, and Trust: Rethinking E-Commerce Support with Generative AI and Ethical Messaging
Kole, D., Kapoor, S., Soni, J. A., Sahoo, I., Kumar, U., Bidkar, D. M., Dontireddy, S. R. R., & Aggarwal, A. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203627 · Cited by 5
“(2018) emphasize the cruciality of impartiality, accountability, and transparency in the processes of algorithmic decision-making [2].”
Towards a simple mathematical model for the legal concept of balancing of interests
Zufall, F., Kimura, R., & Peng, L. (2022) · Artificial Intelligence and Law · doi:10.1007/s10506-022-09338-3 · Cited by 3
“This surely opens our approach up to the general critique that has been raised towards any approach that tries to formalize or quantify legal reasoning (Binns et al. 2018; Deakin and Markou 2020; Hildebrandt 2020; Martínez-Zorrilla 2018; Ronkainen 2011; Selbst et al. 2019).”
BackgroundAlmada, M. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3264189 · Cited by 3
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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