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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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“2Although we acknowledge this is an important challenge if explanations are to be more than just comforting stories, as cautioned by Lipton [41].”
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“6Some readers might have reservations over parametric tests being applied to ordinal data: we point to recent literature that has emphasised [53] that Likert data are robust to parametric assumptions, and that ‘parametric methods can be utilized without concern for “getting the wrong answer”’.”
BackgroundComparing Individual Means in the Analysis of Variance
Tukey, J. W. (1949) · Biometrics · doi:10.2307/3001913 · Cited by 4,302 · cited 1×
“We used ANOVA tests, followed by Tukey’s post-hoc paired tests [75], to analyse the relative effects of different explanation styles within different study designs.”
MethodBias in random forest variable importance measures: Illustrations, sources and a solution
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Cited by
Sorted by citations
Items 201–300 of 696
Navigating a Black Box: Students’ Experiences and Perceptions of Automated Hiring
Armstrong, L., Everson, J., & Ko, A. J. (2023) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3568813.3600123 · Cited by 28
“It is crucial to understand users’ perceptions of algorithmic systems to ensure greater transparency [7].”
BackgroundInfluentialTo Trust or Distrust AI: A Questionnaire Validation Study
Scharowski, N., Perrig, S. A. C., von Felten, N., Aeschbach, L. F., Opwis, K., Wintersberger, P., & Brühlmann, F. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732025 · Cited by 25
“In order to reach the number of participants necessary for a high-impact validation study, we used a scenario-based approach, following prior work on trust [e.g., 5, 32, 36, 39, 81, 82].”
Tausch, A., Kluge, A., & Adolph, L. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.564672 · Cited by 26
“In a series of studies from Binns et al. (2018), many participants perceived an algorithmic decision as odd, impersonal or even dishonorable, whilst for others justice was not a topic, as the system was perceived to only do what it was supposed to do.”
BackgroundBenjamin, J. J., Kinkeldey, C., Müller-Birn, C., Korjakow, T., & Herbst, E.-M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3492858 · Cited by 19
Dexe, J., Franke, U., Söderlund, K., van Berkel, N., Jensen, R. H., Lepinkäinen, N., & Vaiste, J. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-022-00271-9 · Cited by 19
Tensions in transparent urban AI: designing a smart electric vehicle charge point
Alfrink, K., Keller, I., Doorn, N., & Kortuem, G. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01436-9 · Cited by 18
“However, the literature we have reviewed provides a mixed view of the relationships between transparency, understanding, fairness and trust (Binns 2018; Cheng et al. 2019; Kizilcec 2016).”
BackgroundWhat algorithmic evaluation fails to deliver: respectful treatment and individualized consideration
Chun, J. S., Cremer, D. D., Oh, E., & Kim, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-76320-1 · Cited by 25
Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making
Okoso, A., Yang, M., & Baba, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713744 · Cited by 26
“Prediction-related information includes uncertainty of the predictions [25, 39], local feature importance [1, 14], rule-based explanations [38, 40], and example-based explanations [8, 11].”
Trust in Collaborative Automation in High Stakes Software Engineering Work: A Case Study at NASA
Widder, D. G., Dabbish, L., Herbsleb, J. D., Holloway, A., & Davidoff, S. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445650 · Cited by 23
“evaluated users’ perceptions of justice in algorithmic decision making in different scenarios, and found that the facts of the case mattered more to participants than the explanations they were given [3].”
BackgroundMok, L., Nanda, S., & Anderson, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610100 · Cited by 22
“…performance, and preferences [5, 22, 33, 34, 99], whereas in fairness and interpretability research measured perceptions are unsurprisingly of fairness and interpretability themselves [16, 63, 64, 75, 89], as well as related constructs like trust [67], consistency [84], and justice [16].”
BackgroundInfluentialMilella, F., Russo, D. D., & Bandini, S. (2023) · OBM Geriatrics · doi:10.21926/obm.geriatr.2304262 · Cited by 17
Lünich, M., & Keller, B. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658953 · Cited by 27
“However, the impact varies depending on the dimensions of fairness and types of explanations used [11, 25, 75, 77, 79].”
BackgroundWarner, M., Strohmayer, A., Higgs, M., & Coventry, L. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103468 · Cited by 22
Multi-objective Explanations of GNN Predictions
Liu, Y., Chen, C., Liu, Y., Zhang, X., & Xie, S. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00052 · Cited by 18
Lammert, O., Richter, B., Schütze, C., Thommes, K., & Wrede, B. (2024) · Frontiers in Behavioral Economics · doi:10.3389/frbhe.2024.1377075 · Cited by 22
Workshop on Detection and Design for Cognitive Biases in People and Computing Systems
Dingler, T., Tag, B., Karapanos, E., Kise, K., & Dengel, A. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3375159 · Cited by 17
“Only recently, researchers have started investigating the unintended consequences of such systems [2, 8].”
BackgroundEnhancing Strategic Decision-Making With AI
Yesufu, L. O., & Alajlani, S. E. (2025) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-7036-0.ch018 · Cited by 18
AI-Enabled Strategic Transformation and Sustainable Outcomes in Serbian SMEs
Damnjanović, A. M., Rašković, M., Janković, S. D., Jevtić, B., Skoropad, V. N., Marković, Z., Lukić-Vujadinović, V., Injac, Z., & Marinković, S. (2025) · Sustainability · doi:10.3390/su17198672 · Cited by 16
Song, T., Tan, Y., Zhu, Z., Feng, Y., & Lee, Y. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757633 · Cited by 34
Transforming financial decision-making with hybrid artificial intelligence (AI)
Asemi, A., Sebrek, S. S., & Garrido, B. P. (2025) · Management Decision · doi:10.1108/md-05-2025-1403 · Cited by 19
Understanding User Perceptions of Trustworthiness in E-Recruitment Systems
Ogunniye, G., Legastelois, B., Rovatsos, M., Dowthwaite, L., Portillo, V., Vallejos, E. P., Zhao, J., & Jirotka, M. D. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3115670 · Cited by 20
Self-citationMitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents
Fleiß, J., Bäck, E., & Thalmann, S. (2024) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3645057.3645062 · Cited by 20
“Binns et al. (2018) found no effect of different types of explanations on the justice perceptions of an AI decision in the HR context.”
BackgroundAlelyani, T. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1474692 · Cited by 16
gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles
Hatwell, J., Gaber, M. M., & Azad, R. M. A. (2021) · Applied Sciences · doi:10.3390/app11062511 · Cited by 17
“Such tasks are often still found in high-stakes decision making domains, such as medical decision making [5–8]; justice and law [9,10]; financial services [11–13]; and defence and military intelligence [14].”
BackgroundKasinidou, M., Kleanthous, S., Orphanou, K., & Otterbacher, J. (2021) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3430665.3456311 · Cited by 23
“In contrast to the above, Binns et al. [3] showed that explanations might not be the best approach to help people evaluate a system’s fairness, with Edwards and Veale also stressing the fact that the “right to explanation” might not be the best approach towards transparency [11].”
BackgroundMethodResultInfluentialAfter-Action Review for AI (AAR/AI)
Dodge, J. E., Khanna, R., Irvine, J., Lam, K.-H., Mai, T., Lin, Z., Kiddle, N. G., Newman, E., Anderson, A. W., Raja, S., et al. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3453173 · Cited by 19
“Carrying on this spirit, recently researchers have produced a spate of publications based on need-finding or perception interviews meant to identify barriers to adoption (e.g. [5, 8, 28, 80]).”
BackgroundSaxena, N. A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314314 · Cited by 21
Yu, J. H., Ma, Z., & Zhu, L. (2023) · Information Technology and People · doi:10.1108/itp-04-2022-0271 · Cited by 18
Kaponis, A., Μaragoudakis, M., & Sofianos, K. C. (2025) · Computers · doi:10.3390/computers14060211 · Cited by 31
“Deep learning-based recommendations: Deep learning techniques are increasingly being used to model complex user-object interactions, successfully leading to more accurate and relevant content recommendations [29,30,67].”
Buschek, D., Eiband, M., & Hußmann, H. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3519264 · Cited by 16
“reveal existence of intelligent decision-making and processing)?, How fair and controllable is the system? [94], How fair is a system decision? [15] or Can I trust this model? [96] and Should I trust this prediction? [49, 96].”
BackgroundNaudts, L. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658934 · Cited by 19
“The prospect of datafication might be experienced as dehumanizing and reductive, an assassination of self-authorship [8, 60, 89].”
BackgroundMisra, S., Katz, B., Roberts, P. S., Carney, M., & Valdivia, I. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101962 · Cited by 16
Are, C. (2024) · Information, Communication & Society · doi:10.1080/1369118x.2024.2396621 · Cited by 9
“…to the robotic and impersonal governance of content that feel so personal and important to participants’ individual journey, paints an isolating, unfair picture of platform governance, consistently with Binns et al.’s (2018) participants feelings that algorithmic decision-making is dehumanising.”
InfluentialKim, S. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103387 · Cited by 22
FairXAI - A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning
Ramachandranpillai, R., Baeza-Yates, R. A., & Heintz, F. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3528321 · Cited by 18
“Binns et al. [101] conducted three experimental studies to investigate how individuals perceive justice in algorithmic decision-making across different scenarios and explanation methods.”
How Fake News Affect Trust in the Output of a Machine Learning System for News Curation
Heuer, H., & Breiter, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61841-4_2 · Cited by 15
AI Fairness in Action: A Human-Computer Perspective on AI Fairness in Organizations and Society
David De Cremer, A., Narayanan, D., Nagpal, M., McGuire, J., & Schweitzer, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2273673 · Cited by 15
“AI systems are, however, incapable of extending due consideration and relational care to this human tendency – instead, as noted, they can only rely on quantifiable and often reductionistic metrics (cf. Binns et al., 2018).”
BackgroundMitigating AI-induced professional identity threat and fostering adoption in the workplace
Shonhe, L., & Min, Q.-F. (2025) · Ai & Society · doi:10.1007/s00146-024-02170-0 · Cited by 16
“When AI is perceived as a collaborator with transparent processes, professionals are more likely to embrace its use (Binns et al. 2018).”
Pareek, S., Schömbs, S., Velloso, E., & Gonçalves, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713468 · Cited by 20
“We adopted a scenario-based approach which is widely employed in HCI research to elicit user opinions, attitudes, and trust in a controlled manner [2, 7, 31, 38, 62].”
Micro-narratives: A Scalable Method for Eliciting Stories of People’s Lived Experience
Skeggs, A., Mehta, A., Yap, V., Ibrahim, S. B., Rhodes, C., Gross, J. J., Munson, S. A., Klasnja, P., Orben, A., & Slovak, P. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713999 · Cited by 14
Online Content Moderation: Does Justice Need a Human Face?
Katsaros, M., Kim, J., & Tyler, T. R. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210879 · Cited by 16
“Some authors emphasize that it is dehumanizing and reduces a person as a human being to be managed by the algorithm (Binns et al., 2018).”
BackgroundCategorical and Continuous Features in Counterfactual Explanations of AI Systems
Warren, G., Byrne, R. M. J., & Keane, M. T. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3673907 · Cited by 16
“The burgeoning prevalence of automated decision-making in the public and private sectors has led to increased concerns about the fairness, transparency, and trustworthiness of these artificial intelligence (AI) systems [3, 14].”
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
Guo, H., Nguyen, T. H., & Yadav, A. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599290 · Cited by 18
“Counterfactual explanations are preferred by human end-users as these explanations provide actionable recourse in many domains [4, 5, 27].”
BackgroundRevillod, G. (2025) · Systems Research and Behavioral Science · doi:10.1002/sres.3140 · Cited by 18
Elshaer, I. A., AlNajdi, S. M., & Salem, M. A. (2025) · Bioengineering · doi:10.3390/bioengineering12101056 · Cited by 17
The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features
Goyal, N., Baumler, C., Nguyen, T. T., & Daumé, H. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645210 · Cited by 20
“[18] further study the explanations styles in [7] and find that local explanations (such as presenting outcomes for similar examples) help surface fairness discrepancies between different cases while global explanations (such as describing how each feature influenced the decision for a given example) increase user con…”
BackgroundAlgorithmic Discrimination From the Perspective of Human Dignity
Orwat, C. (2024) · Social Inclusion · doi:10.17645/si.7160 · Cited by 13
“Algorithmic differentiation often takes on and alters forms of so‐called statistical discrimination (Barocas & Selbst, 2016; Binns et al., 2018).”
BackgroundSong, Y., Li, C., Xing, W., Lyu, B., & Zhu, W. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.101000 · Cited by 18
Kahr, P. K., Rooks, G., Snijders, C. C. P., & Willemsen, M. C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713099 · Cited by 19
Ben‐Michael, E., Greiner, D. J., Huang, M., Imai, K., Jiang, Z., & Shin, S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2505106122 · Cited by 24
Multi-agent Social Choice for Dynamic Fairness-aware Recommendation
Burke, R., Mattei, N., Grozin, V., Voida, A., & Sonboli, N. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3538032 · Cited by 13
“( See [5, 22, 24, 36, 39] for some recent work.)”
Yacoby, Y., Green, B., Griffin, C. L., Jr., & Doshi‐Velez, F. (2022) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v10i1.22001 · Cited by 20
On the Impact of Explanations on Understanding of Algorithmic Decision-Making
Schmude, T., Koesten, L. M., Möller, T. B., & Tschiatschek, S. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594054 · Cited by 22
“3533090 [10] Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt.”
BackgroundJuijn, G., Stoimenova, N., Reis, J., & Nguyen, D. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604677 · Cited by 13
“It is concerned with presenting people with adequate information about the process of how a decision is reached and is therefore closely related to procedural fairness1[5, 10].”
BackgroundInfluentialAgainst the opacity, and for a qualitative understanding, of artificially intelligent technologies
Khalili, M. (2023) · AI and Ethics · doi:10.1007/s43681-023-00332-2 · Cited by 12
“However, it remains unclear why we should assume that different explanation styles—such as input influ-ence-based explanations, demographic-based explanations, case-based explanations, and sensitivity-based explanations [20]—employ intentions, attitudes, beliefs, and desires in their outputs.”
Weith, H., & Matt, C. (2023) · Electronic Markets · doi:10.1007/s12525-023-00668-x · Cited by 12
“Furthermore, explanations are suggested as a driver for fairness perceptions of algorithmic decisions (Binns et al., 2018).”
BackgroundAlgorithmic loafing and mitigation strategies in Human-AI teams
Inuwa-Dutse, I., Toniolo, A., Weller, A., & Bhatt, U. (2023) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2023.100024 · Cited by 17
“Transparency in AI often relies on a set of features capable of conveying the intuitive description of the underlying decision process (Binns et al., 2018; Dodge et al., 2019; Ribeiro et al., 2016; Zhang et al., 2020).”
BackgroundApplication of fairness to healthcare, organizational justice, and finance: A survey
Birzhandi, P., & Cho, Y.-S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119465 · Cited by 16
Gender Bias in AI Recruitment Systems: A Sociological-and Data Science-based Case Study
Njoto, S., Cheong, M., Lederman, R., McLoughney, A. J., Ruppanner, L., & Wirth, A. I. (2022) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas55053.2022.10227106 · Cited by 22
Machine Behaviour (Originally Published 2019 by Springer Nature)
Rahwan, I., Cebrián, M., Obradovich, N., Bongard, J., Bonnefon, J., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2022) · Wiley eBooks · doi:10.1002/9781119815075.ch10 · Cited by 12
Aljuneidi, S., Heuten, W., Abdenebaoui, L., Wolters, M. K., & Boll, S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642535 · Cited by 15
“Example domains are loans approval [71, 84], insurance applications [12], health and coaching [7, 34], and criminal justice [29, 44].”
BackgroundMethodInfluentialXuan, Y., Small, E. A., Sokol, K., Hettiachchi, D., & Sanderson, M. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103376 · Cited by 17
Freel, A., Pias, S. B. H., Šabanović, S., & Kapadia, A. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732187 · Cited by 11
Johnson, R., & Zhang, S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533223 · Cited by 20
Dolata, M., & Schwabe, G. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210887 · Cited by 11
“Many people experience injustice due to decisions taken by algorithms (Binns et al., 2018; Eubanks, 2017; K € ochling et al., 2021; Marjanovic et al., 2021; O ’ Neil, 2016).”
BackgroundHao, J., Shi, Q., Ye, Y., & Zeng, W. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327389 · Cited by 11
Method for Appropriating the Brief Implicit Association Test to Elicit Biases in Users
Dingler, T., Tag, B., Eccles, D., van Berkel, N., & Kostakos, V. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517570 · Cited by 11
“A particularly timely goal within the HCI community is the involvement of end-users in the creation and assessment of algorithmic decision-making systems [8, 79].”
BackgroundGemalmaz, M. A., & Yin, M. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534201 · Cited by 10
“Other key influencing factors include whether and how the system’s decisions are explained [3, 10, 38], the ways that the AI system’s decisions are presented and visualized [41], and people’s personal experience related to the algorithmic decision making scenario [14].”
BackgroundLee, S. Y., & Liu, W. (2025) · Telematics and Informatics · doi:10.1016/j.tele.2025.102308 · Cited by 14
Aggarwal, I., Joseph, S., Jaganathan, N., Patel, A., Kumar, V., & Devarapalli, M. R. (2025) · Cureus · doi:10.7759/cureus.88902 · Cited by 13
“NLP systems trained on broad internet corpora can unintentionally reinforce inequities related to race, gender, or socioeconomic status [22].”
Explainability and interpretability of artificial intelligence use in cybersecurity
Ofusori, L. O., Bokaba, T., & Mhlongo, S. (2025) · Discover Computing · doi:10.1007/s10791-025-09760-6 · Cited by 17
“Binns, Van Kleek, Veale, Lyngs, Zhao and Shadbolt [65] also indicate that users tend to prefer coun-terfactual explanations over feature-importance methods, finding them more relatable and actionable for interpreting model outcomes.”
Counterfactual Explanations May Not Be the Best Algorithmic Recourse Approach
Upadhyay, S., Lakkaraju, H., & Gajos, K. Z. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712095 · Cited by 10
“The fndings of other works comparing counterfactuals with different explanation types [3, 14, 20, 32, 34, 47, 55, 58] were less closely tied to our hypotheses.”
Fernández, S., Bodin, U., & Synnes, K. (2025) · Sustainability · doi:10.3390/su17083437 · Cited by 13
Building consumer trust in the ChatGPT’s era: Insights from the hospitality industry
Hassan, H. M., & Magdy, A. (2025) · Tourism and Hospitality Research · doi:10.1177/14673584251343264 · Cited by 12
Managing human–AI tensions in HRM: a learning approach to identify practical AI integration pathways
Talwar, S., Alshaghdali, N. O., Gupta, P., & Singhal, A. B. (2025) · Journal of Enterprise Information Management · doi:10.1108/jeim-03-2025-0139 · Cited by 13
Understanding User Sensemaking in Machine Learning Fairness Assessment Systems
Gu, Z., Yan, J. N., & Rzeszotarski, J. M. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450092 · Cited by 15
“searchers have conducted empirical investigations [13, 38, 48, 60].”
BackgroundAn Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
Jakubik, J., Schoeffer, J., Hoge, V., Vössing, M., & Kühl, N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23618-1_24 · Cited by 15
A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making
Schoeffer, J. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503811 · Cited by 9
“2 Moreover, while the general need for transparency appears obvious, it is also understood that there exists no “one-size-fits-all” explanation technique that addresses all desiderata of different stakeholders of algorithmic decision-making [3, 14, 35].”
Martínez, M. A. M., Nadj, M., Langner, M., Toreini, P., & Maedche, A. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3607145 · Cited by 12
“Furthermore, this domain is highly relevant as inancial institutions increasingly use AI systems to evaluate loan applications [10], and the resulting decisions can signiicantly impact loan applicants.”
BackgroundMethodInfluentialViaña, J. N. M., Pilbeam, C., Howard, M., Scholz, B., Ge, Z., Fisser, C., Mitchell, I., Raman, S., & Leach, J. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0120 · Cited by 8
“Individuals subject to AI decision making have felt that they are being ‘‘reduced to a percentage’’ by algorithms (Bankins et al., 2022; Binns et al., 2018; Formosa et al., 2022).”
Hwang, A. H., & Won, A. S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642202 · Cited by 9
“The task and these employee profles were adopted from Binns et al.’s oft-cited study [16], which has been used in prior research studying human-AI co-decision-making [87].”
MethodEnhancing Consumer Trust Through Transparent Data Practices and Ethical Data Management in Business
EL-Annan, S. H., & Hassoun, R. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-8357-5.ch005 · Cited by 9
Access Denied: Meaningful Data Access for Quantitative Algorithm Audits
Zaccour, J., Binns, R., & Rocher, L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713963 · Cited by 10
Self-citationCao, H. J., Lee, H. R., & Peng, W. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713254 · Cited by 16
“On a broader societal level, media coverage of the ethical considerations of AI and concerns about AI’s impact on law enforcement, surveillance, and financial systems could drive people to oppose AI-enabled systems in society [4, 54].”
A Comprehensive Review of Bias in AI, ML, and DL Models: Methods, Impacts, and Future Directions
Kumar, A., Dhanka, S., Sharma, A., Sharma, A., Nain, M., Kumar, P., Gupta, A. R., Bansal, J., Saxena, N. K., & Pant, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10483-6 · Cited by 11
“…each pillar’s relative maturity and influence in guiding the these issues requires rigorous bias detection, fairness-aware model design, and transparent decision-making processes to ensure that automated credit systems promote equitable financial access and comply with regulatory standards [ 47].”
Teng, Z., Xia, H., & He, Y. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20030213 · Cited by 9
Acceptability and 5G in the Medical Field: The Impact of the Level of Information
Martin, N., Ragot, M., & Savaux, V. (2021) · 2021 Zooming Innovation in Consumer Technologies Conference (ZINC) · doi:10.1109/zinc52049.2021.9499276 · Cited by 5
“Indeed, some authors have previously raised the question of framing information on user perceptions [27]–[29].”
BackgroundAljuneidi, S., Heuten, W., Tepe, M., & Boll, S. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609518 · Cited by 11
ObjectivAIze: Measuring Performance and Biases in Augmented Business Decision Systems
Baudel, T., Verbockhaven, M., Cousergue, V., Roy, G., & Laarach, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85613-7_22 · Cited by 12
Viganò, E., Hertweck, C., Heitz, C., & Loi, M. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534643 · Cited by 10
“We rather point to a prima facie difference, which has been found in people’s perceptions about the morality of algorithmic decision-making [7].”
BackgroundSome HCI Priorities for GDPR-Compliant Machine LearningPreprint
Veale, M., Binns, R., & Kleek, M. G. V. (2018) · doi:10.31228/osf.io/wm6yk · Cited by 9
Self-citationCo-Designing with Users the Explanations for a Proactive Auto-Response Messaging Agent
Jain, P., Farzan, R., & Lee, A. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3604248 · Cited by 8
Understanding the User Perception and Experience of Interactive Algorithmic Recourse Customization
Koh, S., Kim, B. H., & Jo, S. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3674503 · Cited by 8
“Recent studies have shown that understanding diverse aspects of user-AI interaction provides a new perspective on how to meet the actual needs of users when it comes to AI interfaces [7, 33, 46, 57].”
How is "Public Policy" Used in HCI Scholarship?
Gairola, R., & Gray, C. M. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3719997 · Cited by 8
Li, H., Cheng, X., & Xiao-ping, Z. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713526 · Cited by 15
“[7].”
Using AI-Driven Analysis for Training and Leadership Growth
Subrahmanyam, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3658-9.ch013 · Cited by 8
Culturally responsive AI chatbots: From framework to field evidence
Naidoo, V., & CHADHA, K. K. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100224 · Cited by 13
STILE: Exploring and Debugging Social Biases in Pre-trained Text Representations
Kabir, S., Li, L., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642111 · Cited by 8
Enhancing Teacher Professional Development with AI
Kohnke, L. (2024) · Springer briefs in education · doi:10.1007/978-981-97-8839-2_6 · Cited by 8
Desafíos éticos en la adopción de tecnologías emergentes en contabilidad
Hurtado-Guevara, R. F., Blacio, J. H. A., & López-Pérez, P. J. (2023) · Revista Científica Ciencia y Método · doi:10.55813/gaea/rcym/v1/n2/13 · Cited by 8
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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