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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.”
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BackgroundComparing Individual Means in the Analysis of Variance
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Cited by
Sorted by citations
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882
“…a system is explicable (explainable and interpretable) it is inherently more transparent and therefore more accountable in terms of its decision-making properties and the extent to which they include human oversight and are fair, robust and justifiable (Binns et al. 2018; Cath 2018; Lipton 2016).”
BackgroundImproving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777
“For example, HCI researchers have empirically studied users’ expectations and perceptions related to fairness in algorithmic systems, finding that these do not always align with existing statistical definitions [16, 71, 72, 108].”
BackgroundQuestioning the AI: Informing Design Practices for Explainable AI User Experiences
Liao, Q. V., Gruen, D., & Miller, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376590 · Cited by 925
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2020) · arXiv · doi:10.48550/arxiv.2006.14779 · Cited by 754
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757
“n natural language (Section3.5). Admittedly, explanations can also be presented in other ways. For structured data, most studies present the salient features, sometimes paired with importance weights [8, 21, 47, 52, 63]. On vision tasks, some work takes the demonstrate-by-example approach, i.e., by explaining an examp…”
MethodTrustworthy Artificial Intelligence: A Review
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023) · ACM Computing Surveys · doi:10.1145/3491209 · Cited by 709
“Accountability of algorithms is an incremental process [18], requiring proper governance of the AI lifecycle and discussion between different stakeholders [144].”
BackgroundManipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728
Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652
“Regulators call, for instance, for trustworthy systems [5, 6, 10, 11, 29, 96, 115, 131].”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems
Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785
“User Understanding of Model Interview ([40]) and Self-explanation ([20, 47, 164]) Likert-scale Questionnaire ([99, 111, 113, 124, 133, 171]) Model Output Prediction User Prediction of Model Output ([96, 172, 173]) Model Failure Prediction User Prediction of Model Failure ([15, 161])”
BackgroundMethodInfluentialTowards a standard for identifying and managing bias in artificial intelligence
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548
Actionable Recourse in Linear Classification
Ustun, B., Spangher, A., & Liu, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287566 · Cited by 501
“Seeing how the lack of autonomy is perceived as a source of injustice in algorithmic decision-making [6, 14, 33], recourse should be considered whenever humans are subject to the predictions of a machine learning model.”
BackgroundTransparency in Algorithmic and Human Decision-Making: Is There a Double Standard?
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019) · Philosophy & Technology · doi:10.1007/s13347-018-0330-6 · Cited by 420
“Often knowing the reasons why a particular decision has been taken, even if only in rough outline, can engender trust in the process that led to it and confidence that the people in charge of the process acted fairly and reasonably (Binns et al. 2018).”
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Green, B., & Chen, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359152 · Cited by 392
“…making, prior research has explored topics such as what interactions can facilitate the development of machine learning models [9, 29, 47], how to improve human performance with an algorithm’s assistance [12, 37, 48], and the ways in which laypeople perceive algorithmic decisions [4, 28, 52].”
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Yam, K. C., Tang, P. M., Jackson, J. C., Su, R., & Gray, K. J. (2022) · Journal of Applied Psychology · doi:10.1037/apl0001045 · Cited by 368
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Explanations in Autonomous Driving: A Survey
Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355
“b) Explanation Style: explanations are categorised based on the type of information or elements referenced in the explanation and the forms they are presented in [72].”
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Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022) · Big Data & Society · doi:10.1177/20539517221115189 · Cited by 316
“Explanations for an algorithmic decision significantly increased respondents’ perceptions of fairness in several studies (Binns et al., 2018; Dodge et al., 2019; Shin, 2021; Shulner-Tal et al., 2022).”
BackgroundMethodResultInfluentialBias and Discrimination in AI: A Cross-Disciplinary Perspective
Ferrer, X., Nuenen, T. V., Such, J. M., Cote, M., & Criado, N. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056293 · Cited by 308
“[27] find a strong split in their respondents: some find the general idea of algorithmic discrimination immoral, others resist imputing morality to a computer system altogether “the computer is just doing its job” [27].”
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Wang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320
“…the direction of change in the model’s prediction when a feature’s value is altered [16, 18, 29], to simulate the model’s predictions [16, 24, 40, 47, 59], to answer “what-if” questions about the model behavior [7, 16, 24, 33, 54], and to detect mistakes of the model and debug the model [59, 60].”
BackgroundArtificial Intelligence and Patient-Centered Decision-Making
Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274
“It is well-known that “ the provision of explanations can affect levels of trust and acceptance of algorithmic decisions ” (Binns et al. 2018, p. 377).”
BackgroundExplaining models: an empirical study of how explanations impact fairness judgment
Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302310 · Cited by 286
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations
Rong, Y., Leemann, T., Nguyen, T.-T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G., & Kasneci, E. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331846 · Cited by 278
“One of the most critical design choices when assessing objective understanding is the selection of a suitable proxy task.”
BackgroundThe flaws of policies requiring human oversight of government algorithms
Green, B. (2022) · Computer Law & Security Review · doi:10.1016/j.clsr.2022.105681 · Cited by 263
“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).”
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“Another use case related to the flight re-routing system offers the latest flight data to users so that travel is flexible and comfortable (Binns et al., 2018).”
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Lee, M. K., Jain, A., Jin, H., Ojha, S., & Kusbit, D. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359284 · Cited by 267
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“…with the lack of human interaction often attendant in AI decision making, can lead workers to think that they are “being reduced to a percentage” (Binns et al., 2018, p. 1; Lee et al., 2019, p. 16; Lee, 2018); which constitutes a set of feelings often captured under the umbrella term of…”
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“This is particularly challenging because the systems are not usually designed “with the information needs of those individuals facing significant personal consequences of model outputs” [7:10].”
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De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221
Efficient Search for Diverse Coherent Explanations
Russell, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287569 · Cited by 218
“Binns et al. (2018) found evidence that users prefer counterfactual explanations over case-based reasoning.”
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Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., & Riedl, M. O. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302316 · Cited by 210
Lee, M. K., Kusbit, D., Kahng, A., Kim, J. T., Yuan, X., Chan, A., See, D., Noothigattu, R., Lee, S., Psomas, A., et al. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359283 · Cited by 260
Brown, A., Chouldechova, A., Putnam-Hornstein, E., Tobin, A., & Vaithianathan, R. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300271 · Cited by 207
Highly automated job interviews: Acceptance under the influence of stakes
Langer, M., König, C. J., & Papathanasiou, M. (2019) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12246 · Cited by 203
“It seems possible that participants might have interpreted that a computer gains decision power over a human being, which may be seen as less fair than a person being in charge of conducting high-stake interviews and making high-stake selection decisions (Binns et al., 2018; M. K. Lee, 2018; Ötting & Maier, 2018).”
InfluentialAI-generated vs. Human Artworks. A Perception Bias Towards Artificial Intelligence?
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Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594087 · Cited by 204
“Subjective metrics of understanding typically ask participants to directly rate their understanding of the AI [5, 12, 15, 18, 23, 75, 91, 105, 108], or some variations of it such as confidence in understanding [57], ease of understanding [46, 83], or confidence in simulation [2, 80].”
BackgroundInfluentialThe ethical use of artificial intelligence in human resource management: a decision-making framework
Bankins, S. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09619-6 · Cited by 181
“…denial of human interaction can degrade individuals’ justice perceptions, or the perceived fairness of a decision, generating feelings of impersonal treatment, devaluation, and dehumanization (such as “being reduced to a percentage”) (Binns et al., 2018, p. 1; Lee et al., 2019, p. 16; Lee, 2018).”
BackgroundSrivastava, M., Heidari, H., & Krause, A. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330664 · Cited by 179
Administration by Algorithm? Public Management meets Public Sector Machine Learning
Veale, M., & Brass, I. (2019) · doi:10.31235/osf.io/mwhnb · Cited by 316
“At this level—the level where individual decisions are made concerning citizens and entities—legal regimes and scholars have been concerned about the threats of such automation to procedural justice in both its substance (Crawford & Schultz 2014; Keats Citron & Pasquale 2014) and the perception of it by citizens (Binn…”
InfluentialSelf-citationHow Explainability Contributes to Trust in AI
Ferrario, A., & Loi, M. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533202 · Cited by 163
Fairness and Explanation in AI-Informed Decision Making
Angerschmid, A., Zhou, J., Theuermann, K., Chen, F., & Holzinger, A. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020026 · Cited by 161
Kabir, S., Udo-Imeh, D. N., Kou, B., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642596 · Cited by 160
“Therefore, it gives an extremely verbose response that contains the definitions of encapsulation, inheritance, etc., which is not useful in terms of identifying the differences originally asked for.”
BackgroundHow Do Fairness Definitions Fare?
Saxena, N. A., Huang, K., DeFilippis, E., Radanović, G., Parkes, D. C., & Liu, Y. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314248 · Cited by 222
“…in targeted online advertising, Grgić-Hlača et al. (2018a) and Grgić-Hlača et al. (2018b) studied human perceptions of features used in algorithmic decision making, and Binns et al. (2018) examined people’s perception of justice in algorithmic decision making under different explanation styles.”
BackgroundMarcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372867 · Cited by 159
“Some studies have looked into the determinants of distributive fairness perceptions and have found that process-related variables impact how people evaluate the outcome of ADM [8,16].”
BackgroundMethodInfluentialHow to explain AI systems to end users: a systematic literature review and research agenda
Laato, S., Tiainen, M., Najmul Islam, A. K. M., & Mäntymäki, M. (2022) · Internet Research · doi:10.1108/intr-08-2021-0600 · Cited by 155
Discrimination, artificial intelligence, and algorithmic decision-making
Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152
AI Systems and Respect for Human Autonomy
Laitinen, A., & Sahlgren, O. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.705164 · Cited by 154
“Empirical research also supports the notion that algorithmic decisions involve risks of objecti fi cation, of individuals being reduced to mere numbers or percentages, as it were (Binns et al., 2018).”
BackgroundUnderstanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Chen, V., Liao, Q. V., Wortman Vaughan, J., & Bansal, G. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610219 · Cited by 220
“This approach helps people understand not only how the model makes decisions but also how it might make mistakes [8, 25, 49].”
BackgroundMa, S., Lei, Y., Wang, X., Zheng, C., Shi, C., Yin, M., & Ma, X. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581058 · Cited by 143
The impact of using algorithms for managerial decisions on public employees' procedural justice
Nagtegaal, R. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2020.101536 · Cited by 166
Adversarial XAI Methods in Cybersecurity
Kuppa, A., & Le‐Khac, N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3117075 · Cited by 151
Artificial intelligence explainability: the technical and ethical dimensions
McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2020.0363 · Cited by 136
“Research suggests that the provision of sufficiently detailed explanations can affect the acceptance of algorithmic decisions by users—primarily expert-users [25].”
BackgroundHuman-LLM Collaborative Annotation Through Effective Verification of LLM Labels
Wang, X., Kim, H., Rahman, S., Mitra, K., & Miao, Z. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641960 · Cited by 188
Artificial intelligence and the value of transparency
Walmsley, J. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01066-z · Cited by 131
Formosa, P., Rogers, W., Griep, Y., Bankins, S., & Richards, D. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107296 · Cited by 129
“This finding fits with existing research about the dehumanizing impacts of AI decision making (Binns et al., 2018) and perceptions about the inap-propriateness of AI making some morally significant decisions (Formosa & Ryan, 2021).”
BackgroundResultInfluentialHuman Judgment in algorithmic loops: Individual justice and automated decision‐making
Binns, R. (2022) · Regulation & Governance · doi:10.1111/rego.12358 · Cited by 134
Self-citationMorley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2021) · Philosophical studies series · doi:10.1007/978-3-030-81907-1_10 · Cited by 202
Molina, M. D., & Sundar, S. S. (2024) · New Media & Society · doi:10.1177/14614448221103534 · Cited by 124
“…set of studies, participants expressed more acceptance of AI compared with humans for content moderation because decisions made by AI are at least based on the same constant rules being applied (Binns et al., 2018; Jhaver et al., 2018), and, thus, are “statisti-cally fair” (Binns et al., 2018: 9).”
Background"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Lai, V., Liu, H., & Tan, C. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376873 · Cited by 123
“In addition to studies mentioned in the introduction, researchers have investigated other desiderata of explanations [5, 8, 17, 18, 32, 49, 65].”
BackgroundHuman-XAI Interaction: A Review and Design Principles for Explanation User Interfaces
Chromik, M., & Butz, A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_36 · Cited by 147
“They emphasize either (i) the descriptive accuracy of an explanation to describe the underlying AI behavior [26, 30, 48, 56, 68] or (ii) the capacity of a single explanation style [4] or differences between explanation styles [9, 14, 23, 25, 106, 83] to convey information about the behavior to the human.”
BackgroundThe Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.-H., Muller, M., & Riedl, M. O. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642474 · Cited by 123
Helberger, N., Araujo, T., & de Vreese, C. H. (2020) · Computer law & security review · doi:10.1016/j.clsr.2020.105456 · Cited by 142
Springer, A., & Whittaker, S. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302322 · Cited by 115
“These systems provide information ranging from routes to work to recommendations about criminal parole [3,8].”
BackgroundExploring Explainability: A Definition, a Model, and a Knowledge Catalogue
Chazette, L., Brunotte, W., & Speith, T. (2021) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re51729.2021.00025 · Cited by 142
XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
Lopes, P., Silva, E., Braga, C., Oliveira, T., & Rosado, L. (2022) · Applied Sciences · doi:10.3390/app12199423 · Cited by 112
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111
“Research by social scientists and humanities scholars has similarly advanced the state of theory and application [5, 11, 80, 136, 149].”
BackgroundResponsible AI Systems: Who are the Stakeholders?
Deshpande, A., & Sharp, H. (2022) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3514094.3534187 · Cited by 123
“The need for the HCI community to draw on organisational behavioural psychology literature to inform the design of AI systems and their decision-making is highlighted by [45] [11] [31].”
Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook
Yildirim, N., Pushkarna, M., Goyal, N., WATTENBERG, M. P., & Viégas, F. B. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580900 · Cited by 126
“AI products1 canmake inference errors that are difficult to anticipate, leading to unpredictable behaviors, breakdowns in user experience (UX) and harm [10].”
BackgroundApplicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures
Lavanchy, M., Reichert, P., Narayanan, J., & Savani, K. (2023) · Journal of Business Ethics · doi:10.1007/s10551-022-05320-w · Cited by 145
“Recent work studying justice perceptions of algorithms has found that there is ‘no best approach’ in explaining algorithmic decisions (Binns et al., 2018).”
BackgroundResultFrom Responsibility to Reason-Giving Explainable Artificial Intelligence
Baum, K., Mantel, S., Schmidt, E., & Speith, T. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00510-w · Cited by 103
“…problems like those mentioned in the previous paragraphs; they provide arguments for XAI from the broader context of morality or society in general (e.g., Asaro, 2015; Binns et al., 2018; Cave et al., 2018; Floridi et al., 2018; Langer, Oster, et al., 2021; Lipton, 2018; Wachter et al., 2017).”
BackgroundAnik, A. I., & Bunt, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445736 · Cited by 103
“Evaluating the Impact of Explanations In parallel to developing different explanation approaches, numerous studies have investigated the impact of explanations on user perceptions of and interactions with the systems [5,14,20,33,59,60,86,102,105].”
BackgroundMethodInfluentialAlgorithmic discrimination in the credit domain: what do we know about it?
García, A. C. B., García, M., & C, R. R. B. (2023) · AI & Society · doi:10.1007/s00146-023-01676-3 · Cited by 141
“Binns et al. (2018) developed an empirical lab study asking 19 UK participants for their perception of fairness upon decisions proposed by an ML algorithm in three different scenarios, including credit loan application.”
DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98
“behaviors will be shaped, in part, by folk theories they hold about the workings of a given algorithmic system [26, 28] and by their existing perceptions and beliefs about algorithmic bias and unfairness in a given context [12, 15, 24, 38, 75, 80] — both of which are”
BackgroundShifting Concepts of Value: Designing Algorithmic Decision-Support Systems for Public Services
Holten Møller, N., Shklovski, I., & Hildebrandt, T. T. (2020) · Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society · doi:10.1145/3419249.3420149 · Cited by 97
“Metrics can show an improvement in the public registered status of a person, the number of complaints over a process, or if criteria were met for due process [3, 34, 36].”
BackgroundExplainable AI for Industry 5.0: Vision, Architecture, and Potential Directions
Trivedi, C., Bhattacharya, P., Prasad, V. K., Patel, V., Singh, A., Tanwar, S., Sharma, R., Aluvala, S., Pau, G., & Sharma, G. (2024) · IEEE Open Journal of Industry Applications · doi:10.1109/ojia.2024.3399057 · Cited by 110
“To determine how users perceive fairness in rational judgments, Binns et al. [42] studied explanations in systems for everyday tasks such as calculating premium rates and loan application clearances.”
Background”Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India
Kapania, S., Siy, O., Clapper, G., SP, A. M., & Sambasivan, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517533 · Cited by 90
An Overview of Fairness in Clustering
Chhabra, A., Masalkovaitė, K., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3114099 · Cited by 116
“There is also a lot of scope in borrowing from similar efforts that assess perceived fairness in algorithmic decision-making systems such as [186].”
BackgroundHuman intervention in automated decision-making: Toward the construction of contestable systems
Almada, M. (2019) · Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law · doi:10.1145/3322640.3326699 · Cited by 95
Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?
Springer, A., & Whittaker, S. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3374218 · Cited by 84
“These systems provide information ranging from route planning to recommendations about criminal parole [3, 8, 10, 64].”
BackgroundEffect of Information Presentation on Fairness Perceptions of Machine Learning Predictors
van Berkel, N., Goncalves, J., Russo, D., Hosio, S., & Skov, M. B. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445365 · Cited by 84
“A case-based explanation, in which a representative example from the training data is presented, resulted in the most consistent negative impact on participants’ justice perceptions [7].”
BackgroundFairness Perceptions of Artificial Intelligence: A Review and Path Forward
Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210890 · Cited by 99
“:3) ca et al. (2018b) and Binns et al. (2018), it was found that when fairness perceptions of the decision-making process are high (procedural fairness), this in turn also generally promoted fairness perceptions of decision outcome (distributive fairness).”
BackgroundMaking data science systems work
Passi, S., & Sengers, P. (2020) · Big Data & Society · doi:10.1177/2053951720939605 · Cited by 89
“One reason for this is that the perspective of people facing the most significant effects from a system’s output is often not included in the system’s design and development process (Binns et al., 2018).”
BackgroundHuman-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization
Fan, M., Yang, X., Yu, T., Liao, Q. V., & Zhao, J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3512943 · Cited by 81
“Following this collaborative perspective, researchers studied how people perceive algorithmic decision assistance [10, 52], how they use AI systems for decision-making [13, 14], often in unexpected or suboptimal ways [18, 93], and how to improve the human-AI joint decision-making outcomes [8, 89, 95].”
BackgroundSelf-citationXAIR: A Framework of Explainable AI in Augmented Reality
Xu, X., Yu, A., Jonker, T. R., Todi, K., Lu, F., Qian, X., Belo, J. M. E., Wang, T., Li, M., Mun, A., et al. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581500 · Cited by 96
“Explanations can fulfill users’ curiosity by providing more information [33, 115, 172].”
BackgroundVaccaro, K., Sandvig, C., & Karahalios, K. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415238 · Cited by 102
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., & Tsirtsis, S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445912 · Cited by 89
Choi, D., Lee, S., Kim, S.-I., Lee, K., Yoo, H. J., Lee, S., & Hong, H. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641989 · Cited by 84
“Propelled by research examining AI bias related to gender and race [9, 34, 42, 63, 72], recent years have seen growing attention to AI’s inclusivity toward identity-based minority groups, such as people with disabilities [38–40, 51, 96, 99, 101, 109] and LGBTQ communities [27, 82, 103].”
BackgroundEffects of Explanations in AI-Assisted Decision Making: Principles and Comparisons
Wang, X., & Yin, M. (2022) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3519266 · Cited by 74
“Typical methods include asking people to rank the input data features based on their influence to overall predictions [21, 37], to indicate the direction of change in the model’s prediction when a feature’s value is altered [18, 21, 33], to simulate the model’s predictions [18, 27, 47, 54, 68], to answer “what-if” que…”
BackgroundMethodCrowdsourcing Perceptions of Fair Predictors for Machine Learning
van Berkel, N., Gonçalves, J., Hettiachchi, D., Wijenayake, S., Kelly, R., & Kostakos, V. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359130 · Cited by 74
Yurrita, M., Draws, T., Balayn, A., Murray-Rust, D., Tintarev, N., & Bozzon, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581161 · Cited by 108
Leading with AI in critical care nursing: challenges, opportunities, and the human factor
Hassan, E. A., & El-Ashry, A. M. (2024) · BMC Nursing · doi:10.1186/s12912-024-02363-4 · Cited by 72
“The need for transparency is further supported by a study conducted by Binns et al. (2018) [33], which found that healthcare professionals are likelier to trust and utilize AI systems that provide understandable and transparent decision-making processes.”
BackgroundAlgorithms in the public sector. Why context matters
Wenzelburger, G., König, P. D., Felfeli, J., & Achtziger, A. (2022) · Public Administration · doi:10.1111/padm.12901 · Cited by 85
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
Schoeffer, J., De‐Arteaga, M., & Kühl, N. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642621 · Cited by 105
“At the time of taking the survey, 13.5% of participants were 18–24 years old, 32.6% were 25–34 years old, 21.3% between 35–44, 13.8% between 45– 54,”
BackgroundMethodInfluentialHow Experienced Designers of Enterprise Applications Engage AI as a Design Material
Yildirim, N., Kass, A., Tung, T., Upton, C., Costello, D., Giusti, R., Lacin, S., Lovic, S., O'Neill, J. M., Meehan, R. O., et al. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517491 · Cited by 71
“For example, researchers studied users’ perceptions related to fairness in AI systems [5, 48, 89], and practitioners’ challenges and needs around designing responsible AI [37, 84].”
Background
Metadata
- Authors
- Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N.
- Affiliations
- University of Oxford (GB)
- University of Oxford, Oxford, Oxfordshire , United Kingdom
- University College London (GB)
- Venue
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-21
- Pages
- 1-14
- OpenAlex
- W4296978576
- Semantic Scholar
- aba609e6c5c9b58f9dac272b75983f75a7f190e5
- Cited by
- 691
- References
- 92
- Citations by year
- 2026: 139 · 2025: 204 · 2024: 87 · 2023: 60 · 2022: 76 · 2021: 53 · 2020: 35 · 2019: 31 · 2018: 6
- FWCI
- 56.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Funders
- UK Engineering and Physical Sciences Research Council (EP/J017728/2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- algorithmic decision-making, perceived justice, algorithmic transparency, explanation styles, accountability, arbitrariness, indignity, generalization, automated decision-making, EU data protection law, lending, insurance quotes
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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