Algorithmic Fairness: Choices, Assumptions, and Definitions — TEI-tagged view

Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, Kristian Lum · 2020 · social-sciences · doi:10.1146/annurev-statistics-042720-125902 · TEI xml · tagged edition · index · concepts · authors
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This record’s own method is review, and the tags below follow from that. A review’s findings, quantities and statistics are things stated IN this paper, and some of them report results obtained elsewhere. No tag on this page names a different author. Measured across every review in this corpus, 81% of a review’s tags carry no citation at all — most of them are the review speaking. The corpus does not adjudicate the rest: read an unmarked tag as a claim this paper makes, not necessarily one it made first.
Other fields define these differently the same word, defined another way in another discipline
bias 38 fields · algorithmic fairness 2 fields
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
algorithmic fairness5 equal false positive rates equal positive predictive values calibration within groups sufficiency separation2 bias19 quantitative notions of fairness structural conditions equality of true positive rates equality of false negative rates equal opportunity2 error rate balance equalized odds2 equality of negative predictive value equality of false omission rate equality of positive predictive value equality of false discovery rate predictive parity avoiding disparate mistreatment treatment equality demographic parity2 statistical parity3 group fairness2 Conditional demographic parity fairness through unawareness2 anticlassification treatment parity metric fairness individual fairness2 m-Lipschitz property perfect metric fairness individual counterfactual fairness conditional counterfactual fairness counterfactual parity review319
Method, as described: a review: collect the published definitions, restate them in one notation, and set the impossibility theorems against the public COMPAS dispute as a worked case

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenona young field in which one word carries several formal meanings that cannot all be satisfied at once, so that two competing analyses of the same tool can both be arithmetically correct— “Their most discussed finding was that COMPAS does not satisfy equal false positive rates by race: Among defendants who did not get rearrested, black defendants were twice as likely to be misclassified as high risk. Based largely on this and other similar findings, they described the tool as biased against blacks. Northpointe (now Equivant)…
claim-typecritique— “Much of the subsequent conversation consisted of either trying to harmonize these definitions of fairness or asserting that one or the other is correct. As it turns out, there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality.”
method — evidence— “The goal of this article is not to advance axiomatic definitions of fairness but to summarize the definitions and results in this area that have been formalized to date.”
object of study — evidence— “The rapid growth of this new field has led to wildly inconsistent motivations, terminology, and notation, presenting a serious challenge for cataloging and comparing definitions. This article attempts to bring much-needed order.”
research question — evidence— “The goal of this article is not to advance axiomatic definitions of fairness but to summarize the definitions and results in this area that have been formalized to date.”
contribution — evidence— “Although each flavor of fairness definition presented in this section formalizes an intuitive notion of fairness, these definitions are not mathematically or morally compatible in general. In this section, we review several impossibility results about definitions of fairness”
mechanismTHE INCOMPATIBILITY IS NOT A PROPERTY OF ALGORITHMS. `the trade-off between different notions of fairness would apply even if a human were the one making the prediction`, and `Rejecting model-driven or automated decision-making is not a way to avoid these problems`. It is a property of predicting at all, under inequality— “Notably, scholars have emphasized that the trade-off between different notions of fairness would apply even if a human were the one making the prediction (Kleinberg et al. 2016). Rejecting model-driven or automated decision-making is not a way to avoid these problems.”mechanisms[0]
mechanismand the conditions under which the definitions COULD agree are named and are both unreachable: equal base rates or perfect prediction. The paper calls them `trivial, degenerate, or even utopian (representing two very different utopias)`— “Equal base rates and perfect prediction can be called trivial, degenerate, or even utopian (representing two very different utopias).”mechanisms[1]
contradicts both positions in the COMPAS dispute, insofar as either claimed to be THE fairness answer - 'there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality'— “Much of the subsequent conversation consisted of either trying to harmonize these definitions of fairness or asserting that one or the other is correct. As it turns out, there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality.”claim_relations[0]
extends the impossibility results of Kleinberg et al. and Chouldechova, restated here in one notation and set against a public case— “Putting this in context and recalling that sufficiency implies equality of positive predictive values and that separation implies equality of false positive rates, this result shows that both supported definitions of fairness in the COMPAS debate can only be met when (a) the rate of recidivism and the distribution of scores are the same for…claim_relations[1]
supports the psychometric literature on test fairness running back to the 1960s, named as the older form of the same question— “Though the algorithmic fairness conversation is somewhat new, it resembles older work. For example, since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance”claim_relations[2]
sensefairness - NOT ONE DEFINITION BUT SEVERAL FORMAL ONES, AND THE PAPER'S RESULT IS THAT THEY CANNOT ALL HOLD: `Although each flavor of fairness definition ... formalizes an intuitive notion of fairness, these definitions are not mathematically or morally compatible in general`— “Although each flavor of fairness definition presented in this section formalizes an intuitive notion of fairness, these definitions are not mathematically or morally compatible in general. In this section, we review several impossibility results about definitions of fairness”senses[0]
sense`bias` - THE PAPER CALLS IT OVERLOADED AND GIVES THE DRIFT: `a now overloaded word that, in popular media, has come to mean that the model's performance (however defined) unjustifiably differs along social axes such as race, gender, and class`. The parenthesis is the point— “In recent years, attention has focused on how consequential predictive models may be biased—a now overloaded word that, in popular media, has come to mean that the model's performance (however defined) unjustifiably differs along social axes such as race, gender, and class.”senses[1]
senseTHE COMPAS DISPUTE - two analyses of one tool, both correct. ProPublica: it `does not satisfy equal false positive rates by race`. Northpointe: it `satisfies equal positive predictive values` and calibration within groups. NEITHER SIDE MISCALCULATED. They used different definitions of the same word— “Their most discussed finding was that COMPAS does not satisfy equal false positive rates by race: Among defendants who did not get rearrested, black defendants were twice as likely to be misclassified as high risk. Based largely on this and other similar findings, they described the tool as biased against blacks. Northpointe (now Equivant)…senses[2]
sense`no harmony` - the paper's verdict on the attempts to reconcile them: `Much of the subsequent conversation consisted of either trying to harmonize these definitions ... or asserting that one or the other is correct. As it turns out, there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality`— “Much of the subsequent conversation consisted of either trying to harmonize these definitions of fairness or asserting that one or the other is correct. As it turns out, there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality.”senses[3]
sensethe formal conditions for agreement, stated exactly: SUFFICIENCY implies equality of positive predictive values, SEPARATION implies equality of false positive rates, and both hold together only if recidivism rates and score distributions are the same across racial groups, or some group never experiences an outcome at all— “Putting this in context and recalling that sufficiency implies equality of positive predictive values and that separation implies equality of false positive rates, this result shows that both supported definitions of fairness in the COMPAS debate can only be met when (a) the rate of recidivism and the distribution of scores are the same for…senses[4]
sensealgorithmic fairness - a name for a research field, and the paper notes the questions are older than it: `it resembles older work ... since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance`— “Though the algorithmic fairness conversation is somewhat new, it resembles older work. For example, since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance”senses[5]
senseequality of true positive rates - equality across groups in the true-positive rate— “The second pair, equality of true positive rates and equality of false negative rates, is equivalent to D⊥A|Y = 1. This definition of fairness takes the perspective of those individuals with outcome defined by one. For example, this is equivalent to the requirement that all individuals who will go on to repay a loan have the same likelihood…senses[6]
senseequality of false negative rates - equality across groups in the false-negative rate— “The second pair, equality of true positive rates and equality of false negative rates, is equivalent to D⊥A|Y = 1. This definition of fairness takes the perspective of those individuals with outcome defined by one. For example, this is equivalent to the requirement that all individuals who will go on to repay a loan have the same likelihood…senses[7]
senseequal opportunity - the paper's name for equality of true-positive and false-negative rates considered alone— “The second pair, equality of true positive rates and equality of false negative rates, is equivalent to D⊥A|Y = 1. This definition of fairness takes the perspective of those individuals with outcome defined by one. For example, this is equivalent to the requirement that all individuals who will go on to repay a loan have the same likelihood…senses[8]
senseerror rate balance - Chouldechova's name for the combined outcome-conditioned fairness notions— “The second pair, equality of true positive rates and equality of false negative rates, is equivalent to D⊥A|Y = 1. This definition of fairness takes the perspective of those individuals with outcome defined by one. For example, this is equivalent to the requirement that all individuals who will go on to repay a loan have the same likelihood…senses[9]
senseequalized odds - Hardt and colleagues' name for the combined outcome-conditioned fairness notions— “The second pair, equality of true positive rates and equality of false negative rates, is equivalent to D⊥A|Y = 1. This definition of fairness takes the perspective of those individuals with outcome defined by one. For example, this is equivalent to the requirement that all individuals who will go on to repay a loan have the same likelihood…senses[10]
senseequality of negative predictive value - equality across groups in negative predictive value— “The other pair of definitions that appear on this margin are equality of positive predictive value and equality of false discovery rate. These are defined by the conditional independence relationship Y⊥A|D = 1. This definition of fairness is also sometimes called predictive parity (Chouldechova 2017), and it is assessed by an outcome test…senses[11]
senseequality of false omission rate - equality across groups in false omission rate— “The other pair of definitions that appear on this margin are equality of positive predictive value and equality of false discovery rate. These are defined by the conditional independence relationship Y⊥A|D = 1. This definition of fairness is also sometimes called predictive parity (Chouldechova 2017), and it is assessed by an outcome test…senses[12]
senseequality of positive predictive value - equality across groups in positive predictive value— “The other pair of definitions that appear on this margin are equality of positive predictive value and equality of false discovery rate. These are defined by the conditional independence relationship Y⊥A|D = 1. This definition of fairness is also sometimes called predictive parity (Chouldechova 2017), and it is assessed by an outcome test…senses[13]
senseequality of false discovery rate - equality across groups in false discovery rate— “The other pair of definitions that appear on this margin are equality of positive predictive value and equality of false discovery rate. These are defined by the conditional independence relationship Y⊥A|D = 1. This definition of fairness is also sometimes called predictive parity (Chouldechova 2017), and it is assessed by an outcome test…senses[14]
sensepredictive parity - another name for equality of positive predictive value and false discovery rate— “The other pair of definitions that appear on this margin are equality of positive predictive value and equality of false discovery rate. These are defined by the conditional independence relationship Y⊥A|D = 1. This definition of fairness is also sometimes called predictive parity (Chouldechova 2017), and it is assessed by an outcome test…senses[15]
senseavoiding disparate mistreatment - Zafar and colleagues' collective name for the four predictive-measure pairs plus equal accuracy— “Zafar et al. (2017) call all four pairs of definitions (as well as equal accuracy) avoiding disparate mistreatment. Berk et al. (2017) also consider a definition based on several of the above elements of the confusion matrix. They define treatment equality in terms of the ratio of false negatives to false positives”senses[16]
sensetreatment equality - a fairness definition based on the ratio of false negatives to false positivessenses[17]
sensedemographic parity - equal decision rates across groups irrespective of outcome
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[18]
sensestatistical parity - an alternative name for demographic parity
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[19]
sensegroup fairness - an alternative name for demographic parity
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[20]
senseConditional demographic parity - parity within strata, expressed as conditional independence of decision and sensitive group
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[21]
sensefairness through unawareness - conditional demographic parity when conditioning on insensitive variables
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[22]
senseanticlassification - conditional demographic parity when conditioning on insensitive variables
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senses[23]
sensetreatment parity - conditional demographic parity when conditioning on insensitive variables
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[24]
sensemetric fairness - requiring closeness under a similarity metric to imply closeness in decisions
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[25]
senseindividual fairness - an alternative name for metric fairness
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[26]
sensem-Lipschitz property - an alternative mathematical name for metric fairness
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[27]
senseperfect metric fairness - Rothblum and Yona's name for metric fairness
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senses[28]
senseindividual counterfactual fairness - the strongest listed counterfactual criterion, requiring decision equality for every individual
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senses[29]
senseconditional counterfactual fairness - the intermediate counterfactual criterion, requiring conditional mean-decision equality
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senses[30]
sensecounterfactual parity - the weakest listed counterfactual criterion, requiring population mean-decision equality
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senses[31]
controlwhether the incompatibility is an artefact of using algorithms at all — held
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— rules out the algorithmic explanation - the trade-off 'would apply even if a human were the one making the prediction', so refusing automation does not avoid it
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controls[0]
controlunder what conditions the two COMPAS criteria COULD both hold, worked out formally — held
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— rules out any hope of reconciliation in practice - it requires equal recidivism rates across racial groups, or a group that never experiences an outcome, or perfect prediction
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controls[1]
controlwhether formal fairness work is worth doing at all, against its own critics — partial
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
— rules out nothing on either side, and the paper says so: the criticisms are 'rightly pointed out', and formalism 'should not be dismissed as necessarily preserving the status quo'. Both are asserted; neither is tested
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
controls[2]
weightboth positions in the COMPAS dispute, insofar as either claimed to be THE fairness answer - 'there can be no harmony among definitions in a world where inequality and imperfect prediction are the reality' — 0.9
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[0]
weightthe impossibility results of Kleinberg et al. and Chouldechova, restated here in one notation and set against a public case — 0.8
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[1]
weightthe psychometric literature on test fairness running back to the 1960s, named as the older form of the same question — 0.6
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[2]
edgeequal false positive rates —distinct-from→ equal positive predictive values
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[0]
edgequantitative notions of fairness —contrasted-with→ structural conditions
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concept_edges[1]
auditequality of true positive rates —renamed-across-fields→ equal opportunity — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says this condition 'has been called equal opportunity' by one author; a name, and renamed-across-fields is loose.
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concept_edges[2]
noteequality of true positive rates —renamed-across-fields→ equal opportunity — The paper reports Hardt et al.'s unhedged name for this condition.
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concept_edges[2]
noteseparation —renamed-across-fields→ error rate balance — The paper gives source-specific names for the same combined outcome-conditioned fairness notions.
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concept_edges[3]
noteseparation —renamed-across-fields→ equalized odds — The paper gives source-specific names for the same combined outcome-conditioned fairness notions.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[4]
auditConditional demographic parity —same-as→ separation — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but conditional demographic parity equals separation only when Data = Y; same-as is conditional.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[5]
holds whenConditional demographic parity —same-as→ separation — Data = Y
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concept_edges[5]
noteConditional demographic parity —same-as→ separation — The equivalence is asserted without epistemic hedge but explicitly scoped to conditioning on outcome Y.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[5]
auditConditional demographic parity —same-as→ fairness through unawareness — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: 'it' = conditional demographic parity (previous sentence); the equivalence holds only when Data = X.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[6]
holds whenConditional demographic parity —same-as→ fairness through unawareness — Data = X (the insensitive variables)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[6]
noteConditional demographic parity —same-as→ fairness through unawareness — The equivalence is asserted without epistemic hedge under the paper's explicit Data = X condition.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[6]
auditConditional demographic parity —same-as→ anticlassification — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: 'it' = conditional demographic parity (previous sentence); the equivalence holds only when Data = X.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[7]
holds whenConditional demographic parity —same-as→ anticlassification — Data = X (the insensitive variables)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[7]
noteConditional demographic parity —same-as→ anticlassification — The equivalence is asserted without epistemic hedge under the paper's explicit Data = X condition.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[7]
auditConditional demographic parity —same-as→ treatment parity — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: 'it' = conditional demographic parity (previous sentence); the equivalence holds only when Data = X.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[8]
holds whenConditional demographic parity —same-as→ treatment parity — Data = X (the insensitive variables)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[8]
noteConditional demographic parity —same-as→ treatment parity — The equivalence is asserted without epistemic hedge under the paper's explicit Data = X condition.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[8]
notemetric fairness —same-as→ individual fairness — The paper unhedgedly says metric fairness is also known as individual fairness.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[9]
notemetric fairness —same-as→ m-Lipschitz property — The paper unhedgedly gives the m-Lipschitz property as another name for metric fairness.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[10]
notemetric fairness —same-as→ perfect metric fairness — The paper unhedgedly gives perfect metric fairness as another name for metric fairness.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concept_edges[11]
outcomecontrol — held
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Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
controls[0]
outcomecontrol — held
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
controls[1]
outcomecontrol — partial
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
controls[2]
no type for thisCalibration within groups → sufficiency — The paper says calibration within groups implies sufficiency, and describes a converse after applying a function to a sufficient score. Logical implication between criteria is not one of the eight edge types.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
unhoused_relations[0]
no type for thismetric fairness → unawareness — When the metric uses only insensitive variables, the paper says metric fairness implies unawareness. Logical implication is not one of the eight edge types.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
unhoused_relations[1]
no type for thisindividual counterfactual fairness → conditional counterfactual fairness — The paper orders individual counterfactual fairness above conditional counterfactual fairness in strength. A stronger-than relation is not one of the eight edge types.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
unhoused_relations[2]
no type for thisconditional counterfactual fairness → counterfactual parity — The paper orders conditional counterfactual fairness above counterfactual parity in strength. A stronger-than relation is not one of the eight edge types.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
unhoused_relations[3]
limitationthe paper declines the job most readers want done: `The goal of this article is not to advance axiomatic definitions of fairness but to summarize the definitions and results ... formalized to date`. It catalogues a disagreement rather than settling it, and says so in the introduction
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limitations[0]
limitationand it names the gap its own subject cannot close: `gaps between mathematically convenient formalism and the larger social goals that many of these concepts were introduced to address`. THAT IS THE OPERATIONALISATION GAP, NAMED AS SUCH, by a paper whose subject is operationalisations
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limitations[1]
limitationthe object being predicted is already an abstraction of the thing anyone cares about: policy goals are `sometimes clumsily, abstracted and formulated to fit into a prediction task`, and the choices that do it sit outside the model
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limitations[2]
limitationand the decision itself is flattened before any of this begins: a judge's options are many, `in this literature, this decision is often reduced to A BINARY DECISION - RELEASE OR DETAIN`. Every fairness metric in the catalogue is defined over that binary
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limitations[3]
limitationthe authors record the standing criticism of their own field without dismissing it: quantitative notions of fairness `can funnel our thinking into narrow silos where we aim to make adjustments to a decision-making process, rather than to address the structural conditions that sustain inequality in society`
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limitations[4]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[0]
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
finding
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content_tags.findings[5]
finding
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content_tags.findings[6]
finding
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content_tags.findings[7]
finding
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content_tags.findings[8]
finding
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content_tags.findings[9]
finding
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content_tags.findings[10]
finding
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content_tags.findings[11]
definitionbias
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content_tags.definitions[0]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptalgorithmic fairness — not defined in this paper's own words; the field name, given to work whose questions psychometrics has asked since the 1960s
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concepts[0]
conceptequal false positive rates — not defined in this paper's own words; ProPublica's fairness criterion - among those who did not get rearrested, equal misclassification by race
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concepts[1]
conceptequal positive predictive values — not defined in this paper's own words; Northpointe's criterion - among those called higher risk, an equal proportion rearrested, regardless of race
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concepts[2]
conceptcalibration within groups — not defined in this paper's own words; the third criterion COMPAS satisfies, and one of the three in Kleinberg's impossibility result
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concepts[3]
conceptsufficiency — not defined in this paper's own words; the condition that implies equality of positive predictive values
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concepts[4]
conceptseparation — not defined in this paper's own words; the condition that implies equality of false positive rates
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concepts[5]
conceptbias — not defined in this paper's own words; declared OVERLOADED - in popular media it has come to mean that model performance, however defined, unjustifiably differs along social axes
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concepts[6]
conceptquantitative notions of fairness — not defined in this paper's own words; the formal definitions collectively, and the thing criticised for funnelling attention into adjustments rather than structures
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concepts[7]
conceptstructural conditions — not defined in this paper's own words; what the formal definitions are accused of directing attention away from
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concepts[8]
conceptequality of true positive rates — not defined in this paper's own words; equality across groups in the true-positive rate
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concepts[9]
conceptequality of false negative rates — not defined in this paper's own words; equality across groups in the false-negative rate
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concepts[10]
conceptequal opportunity — not defined in this paper's own words; the paper's name for equality of true-positive and false-negative rates considered alone
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concepts[11]
concepterror rate balance — not defined in this paper's own words; Chouldechova's name for the combined outcome-conditioned fairness notions
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concepts[12]
conceptequalized odds — not defined in this paper's own words; Hardt and colleagues' name for the combined outcome-conditioned fairness notions
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concepts[13]
conceptequality of negative predictive value — not defined in this paper's own words; equality across groups in negative predictive value
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concepts[14]
conceptequality of false omission rate — not defined in this paper's own words; equality across groups in false omission rate
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[15]
conceptequality of positive predictive value — not defined in this paper's own words; equality across groups in positive predictive value
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[16]
conceptequality of false discovery rate — not defined in this paper's own words; equality across groups in false discovery rate
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[17]
conceptpredictive parity — not defined in this paper's own words; another name for equality of positive predictive value and false discovery rate
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concepts[18]
conceptavoiding disparate mistreatment — not defined in this paper's own words; Zafar and colleagues' collective name for the four predictive-measure pairs plus equal accuracy
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concepts[19]
concepttreatment equality — not defined in this paper's own words; a fairness definition based on the ratio of false negatives to false positives
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concepts[20]
conceptdemographic parity — not defined in this paper's own words; equal decision rates across groups irrespective of outcome
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concepts[21]
conceptstatistical parity — not defined in this paper's own words; an alternative name for demographic parity
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concepts[22]
conceptgroup fairness — not defined in this paper's own words; an alternative name for demographic parity
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concepts[23]
conceptConditional demographic parity — not defined in this paper's own words; parity within strata, expressed as conditional independence of decision and sensitive group
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concepts[24]
conceptfairness through unawareness — not defined in this paper's own words; conditional demographic parity when conditioning on insensitive variables
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concepts[25]
conceptanticlassification — not defined in this paper's own words; conditional demographic parity when conditioning on insensitive variables
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concepts[26]
concepttreatment parity — not defined in this paper's own words; conditional demographic parity when conditioning on insensitive variables
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concepts[27]
conceptmetric fairness — not defined in this paper's own words; requiring closeness under a similarity metric to imply closeness in decisions
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concepts[28]
conceptindividual fairness — not defined in this paper's own words; an alternative name for metric fairness
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concepts[29]
conceptm-Lipschitz property — not defined in this paper's own words; an alternative mathematical name for metric fairness
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concepts[30]
conceptperfect metric fairness — not defined in this paper's own words; Rothblum and Yona's name for metric fairness
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concepts[31]
conceptindividual counterfactual fairness — not defined in this paper's own words; the strongest listed counterfactual criterion, requiring decision equality for every individual
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concepts[32]
conceptconditional counterfactual fairness — not defined in this paper's own words; the intermediate counterfactual criterion, requiring conditional mean-decision equality
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concepts[33]
conceptcounterfactual parity — not defined in this paper's own words; the weakest listed counterfactual criterion, requiring population mean-decision equality
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concepts[34]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
false positive rates
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content_tags.statistics[0]
positive predictive values
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content_tags.statistics[1]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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