The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index; 12 of the 33 sources its claims cite were checked for support.
An algorithm for removing sensitive information: Application to race-independent recidivism prediction
Open accessAbstract
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing or augmenting human judgment with computer models in high stakes settings—such as sentencing, hiring, policing, college admissions, and parole decisions—is the perceived “neutrality” of computers. It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice. There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it if the training data were generated by a process that is itself biased. In this paper, we provide a probabilistic notion of algorithmic bias. We propose a method to eliminate bias from predictive models by removing all information regarding protected variables from the data to which the models will ultimately be trained. Unlike previous work in this area, our procedure accommodates data on any measurement scale. Motivated by models currently in use in the criminal justice system that inform decisions on pre-trial release and parole, we apply our proposed method to a dataset on the criminal histories of individuals at the time of sentencing to produce “race-neutral” predictions of re-arrest. In the process, we demonstrate that a common approach to creating “race-neutral” models—omitting race as a covariate—still results in racially disparate predictions. We then demonstrate that the application of our proposed method to these data removes racial disparities from predictions with minimal impact on predictive accuracy.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a likelihood-based chained transformation that removes information about protected variables from training data, accommodating mixed-scale covariates and targeting mutual independence from race. Applied to Broward County recidivism data, it produces race-independent predictions with only slight loss in predictive accuracy.
Key findings
- Omitting race as a covariate does not yield race-neutral predictions: models trained on unadjusted data still show large racial disparities in predicted re-arrest distributions.
- The proposed adjustment removes most racial dependence: pairwise Cramér’s V statistics are reduced to near zero in adjusted data, and random forest predictions by race become nearly identical.
- Predictive accuracy changes little: random forest AUC was 0.72 unadjusted and 0.71 adjusted, comparable to Northpointe’s reported 0.70 for this dataset.
- Adjustment altered high-risk classification rates: African-American high-risk proportion decreased by 0.07 (0.47 to 0.40), Caucasian increased by 0.14 (0.25 to 0.39), and Hispanic increased by 0.15 (0.27 to 0.42); false-positive-rate variation across race decreased (mad 0.05 to 0.02), while positive predictive value variation increased (mad 0.02 to 0.06).
Methods
- The method transforms covariates \(X\) to new variables \(W\) independent of protected variables \(Z\) by chaining conditional CDF transformations, matching conditional quantiles to marginal quantiles; it handles continuous, count, binary, and other mixed-scale variables and aims for mutual rather than only pairwise independence.
- The application uses ProPublica’s Broward County, Florida recidivism dataset, with race as the protected variable and re-arrest within two years as the outcome; covariates include age, sex, and juvenile/adult prior charge counts.
- The authors estimate conditional distributions with parametric regressions, generate \(K=50\) adjusted datasets, and compare random forest and logistic regression predictions on unadjusted, pairwise-adjusted, and adjusted data using ROC/AUC, calibration-like distribution plots, Cramér’s V, and race-specific accuracy metrics.
Limitations
- The paper states that the conditional distributions \(F_{X_j|V^{(j)}}\) are estimated and may be misspecified in finite samples or asymptotically, so model fit must be checked carefully and a “black box” automated approach is not recommended.
- The paper notes that dependence among variables may change over time, requiring routine re-testing and possible retraining; it also states that re-arrest is a biased measure of re-offense, which motivates the fairness definition but limits outcome validation.
- Evident from the text: the method’s guarantee depends on accurate conditional modeling, and the empirical application is one dataset with a contested demographic-parity fairness criterion; the paper itself notes disagreement among researchers about appropriate fairness notions.
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Claims the paper makes
50 Own interpretation · 34 Own result · 22 No basis given · 5 Not checked yet · 4 Partly supported · 4 No full text of the sources · 3 Supported · 3 Sources not found · 1 Not supported · 1 Couldn’t confirm · 1 Own method · accuracy not yet measured
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“Finally, the procedure requires that all of the covariates X j be continuous variables, further limiting its scope.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Disputedcites Feldman et al. (2015) sourceExperimental — accuracy not yet measured
The Experiments section (pp. 16–17) says ordered categorical features were converted to integers and included in the repair; the procedure requires an ordering, not that every covariate be continuous. The Limitations section (p. 23) describes the algorithm as operating on numerical attributes, which does not establish a continuity requirement. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
plain- Asserted: passes — The authors put it forward as true.
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- Disputed
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Much of the previous work has primarily focused on settings where the outcome or protected variable is binary.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not checked — no full text of the sourcecites Kamiran and Calders (2009) source
- Partly supportedcites Calders and Verwer (2010) sourceExperimental — accuracy not yet measured
The Introduction states that this paper assumes a binary class attribute and a binary sensitive attribute, so it supports the claim as an example of earlier work using binary variables. It does not establish that much previous work primarily focused on such settings. (LLM, conf=0.83)
- Supportedcites Feldman et al. (2015) source
Section 6 (p. 16) describes datasets used in earlier fairness studies with binary outcomes and protected-attribute categories, including GOOD/BAD credit decisions and a YOUNG/OLD age split. Section 3 (p. 4) also frames the paper’s disparate-impact analysis using a binary outcome and two protected-class values. Together, these passages support the broad characterization of the settings emphasized in the work discussed. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In addition to not guaranteeing demographic parity when p > 1, the algorithm has several limitations.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Partly supportedcites Feldman et al. (2015) sourceExperimental — accuracy not yet measured
Section 5 (pp. 11–13) establishes fairness for a single numerical attribute and says multiple attributes are repaired individually. This supports a limitation of the stated guarantee, but the paper does not explicitly say that demographic parity is not guaranteed when p > 1. (LLM, conf=0.78)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“These two notions of fairness are indeed distinct.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Supportedcites Chouldechova (2017) source
Section 2 (p. 2) defines test fairness as a score reflecting the same recidivism likelihood regardless of group. Section 2.1 (p. 2) explains that when recidivism prevalence differs between groups, a test-fair score cannot also have equal false-positive and false-negative rates across those groups. This supports the distinction between the two fairness notions. (LLM, conf=0.93)
- Supportedcites Kleinberg, Mullainathan and Raghavan (2017) source
Section 1.3 (p. 8) distinguishes calibration from symmetry in error rates across groups and says the paper’s results show they can be incompatible. Theorem 1.1 (p. 5) identifies the limited cases in which its fairness conditions can all hold, supporting the claim that these notions are distinct. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“For example, Chouldechova (2017) shows the same positive predictive value by protected class and equal false negative and false positive rates cannot both be achieved when the outcome prevalence depends on protected characteristics.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Supportedcites Chouldechova (2017) source
Section 2.1 (p. 2) states that when recidivism prevalence differs between groups, a test-fair score cannot have equal false-positive and false-negative rates across those groups. The same section defines test fairness here as equal positive predictive value across groups, matching the cited claim. (LLM, conf=0.94)
boosted- Asserted: passes — The authors put it forward as true.
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- Supported
In the paper
“There is strong evidence to reject the null hypothesis of independence for all of the pairs, even when adjusting for multiplicity using the method of Benjamini and Hochberg (1995).”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
What the sources say
- Not checked — no full text of the sourcecites Benjamini and Hochberg (1995) source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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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
“Since functions of independent random variables are independent, X ⊥ ⊥ Z implies Y ⊥ ⊥ Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“and W is mutually independent of Z. Consequently, we refer to (3.2) as a transformation to mutual independence.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.2. Multivariate adjustments via chaining.
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“models fit to the unadjusted data omitting race produce drastically different predictive distributions of the probability of re-offense by race-thus empirically demonstrating the insufficiency of omitting race from the analysis when the goal is statistical parity”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
boosteddirect- 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
“In the example we present below, we have found traditional, parametric regression models to be successful at estimating F X|Z if the analyst employs appropriate model selection and fit diagnostic techniques.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.4. Unknown conditional distributions.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Although f is not a function of Z in (2.1), this is insufficient to guarantee Y ⊥ ⊥ Z unless X ⊥ ⊥ Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“From this it is clear from the unadjusted model that omitting z does little to equalize the distributions of ŷ conditional on z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 4. Simulation example.
boosted- Asserted: passes — The authors put it forward as true.
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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
“When X is multivariate, we chain optimal univariate transformation to create a procedure that achieves mutual independence of W from Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Then, by corollary B.1 the stochastic map W = ζ * (X, Z) for ζ * (X, Z) | X = ẋj ∼ Uniform(ν j -1 , ν j ), achieves (3.1).”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.1. Univariate transformations.
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“If X is continuous, then the transformation W = ζ(X, Z) = G ← (F X|Z (X, Z)) defines the optimal transformation of X to W such that W is independent of Z, W ∼ G, and, informally, W is as similar as possible to X given the independence and distributional constraints”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.1. Univariate transformations.
plaindirect- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Since Ŷ is not a function of Z, we already have Y ⊥ ⊥ Z | X.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 find that random forest or logistic regression fit to only seven transformed variables-mostly pertaining to an individual's criminal history-has substantively equivalent predictive power to proprietary models used for recidivism prediction that use a battery of psychological questionnaires and evaluations in addition to information about the individual's criminal past.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
plain- Asserted: passes — The authors put it forward as true.
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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, for G 1 any normal distribution, cor(W 1 , X 1 ) = 2/ √ 5 ≈ 0.89, demonstrating the high level of remaining dependence between X 1 and W 1 , as desired.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.1.1. Continuous example.
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 particular case of the recidivism data, likelihood-based parametric regression models were more successful.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.4. Unknown conditional distributions.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Opposite trade-offs are observed in the other race categories, with increases in tpp being offset by decreases in tnp for Caucasians and Hispanics.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- 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, a logistic regression model applied to the adjusted datasets result in very similar distributions of fitted values by race.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX C: RECIDIVISM PREDICTION USING LOGISTIC REGRESSION
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
“Further, when fitting both random forest and logistic regression models to the data, we have achieved predictive distributions of recidivism that are approximately independent of race-the ultimate goal of the procedure.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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
“In other words, when trained on unadjusted data omitting race, the model predicts that a larger fraction of the African-American population is at high risk of recidivating than other groups.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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
“Figure 5 gives results for the estimated conditional CDFs by race, all of which are approximately uniform.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“We note that this AUC is on par with the AUC associated with Northpointe's predictions for this dataset (0.70) as reported in Dieterich, Mendoza and Brennan (2016).”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
What the sources say
- Not matchedcites Dieterich, Mendoza and Brennan (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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- Not matched
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
“Even after the adjustment, we observe that the quality of the predictions in terms of overall accuracy like the AUC are on par with methods that are currently in use but do not attempt to adjust for fair predictions.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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
“the proportion of Caucasians considered high risk increases by 0.14, from 0.25 to 0.39”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“When X = R with d the Euclidean norm and F, G have densities, the optimal coupling with respect to c = d q (x, w) for q ≥ 1 is associated with the map ζ(x) = G -1 (ζ * (x)) for ζ * (x) = F (x).”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > REMARK B.1 (Optimal coupling on R).
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
“In this case also, there is little substantive difference between the methods in terms of this measure of predictive accuracy using this metric.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX C: RECIDIVISM PREDICTION USING LOGISTIC REGRESSION
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
“the proportion of Hispanics considered high risk increases by 0.15, from 0.27 to 0.42”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- 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
“the proportion of African-American considered high risk decreases by 0.07, from 0.47 to 0.40”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- 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
“Like we found when using RF, omitting the race variable does little to reduce discrepencies in the distribution of predictions by race.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX C: RECIDIVISM PREDICTION USING LOGISTIC REGRESSION
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.
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 order to achieve ζ(X) ⊥ Z within the class of optimal transport maps above, we must have (B.2) ζ(X, Z) = G ← ζ * X|Z (X, Z) ,”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > REMARK B.1 (Optimal coupling on R).
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“COROLLARY B.1 (Optimal coupling for atomic F using stochastic maps).Suppose X = R with d the Euclidean norm and F is atomic.Let ẋ = { ẋ1 , ẋ2 , . ..} be the support points of F ordered such that ẋj < ẋj+1 , with associated probabilities π j = P[X = ẋj ], and put ν j = j ≤j π j . Define a random map ζ * (X) by ζ * (X) | X = ẋj ∼ Uniform(ν j -1 , ν j ), with ν 0 = 0. Then the random map ζ(X) = G ← (”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > REMARK B.1 (Optimal coupling on R).
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether 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
“We find that these are not substantially different.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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
“We have tested this procedure on a dataset used for recidivism prediction and demonstrated that, by using a series of chained relatively standard regression models, we are able to produce an adjusted dataset in which all pairs of variables in the dataset are approximately independent.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
boosted- 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
“In most cases, Cramer's V is reduced to near zero in the adjusted data, indicating that we have successfully removed information about race from the adjusted data, at least up to two-way interactions.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 mad increases after adjustment for both ppv and npv.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“Under our proposed framework, all model builders, including those acting in bad faith, would be provided a dataset that is independent of race on which to train their model.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
In the paper
“studies suggest that after controlling for criminal behavior, African Americans are more likely than Caucasians to become incarcerated [Bridges and Crutchfield (1988)]”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not checked — no full text of the sourcecites Bridges and Crutchfield (1988) source
hedgedcorrelational- 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 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
“Under this strategy, such an individual would not know or be able to infer the protected status of each individual.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: The average level of criminality is independent of race.
None of the sources cited for this claim have been checked yet.
In the paper
“whether walking or driving, African Americans are disproportionately stopped and searched by police [Simoiu, Corbett-Davies and Goel (2016), Rudovsky (2001)]”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not matchedcites Simoiu, Corbett-Davies and Goel (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites Rudovsky (2001)
Checked before — its earlier verdict is held back until a new check runs.
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.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“For drug crimes, African American drug users are arrested at a rate that is several times that of Caucasian drug users despite the fact that African American and Caucasian populations are estimated by public health researchers to use drugs at roughly the same rate [Langan (1995), Mitchell and Caudy (2015)].”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not matchedcites Langan (1995)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Mitchell and Caudy (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedcorrelational- 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.
- Not matched
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
“In this framework, transforming noncontinuous variables as well as transforming variables for which there is little data corresponding to one or more levels of Z are natural.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
None of the sources cited for this claim have been checked yet.
In the paper
“Typically, post-release re-offense is measured by re-arrest, and there are many reasons to believe that re-arrest may be a biased measure of re-offense with respect to race.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not checked — no full text of the sourcecites Bridges and Crutchfield (1988) source
- Not matchedcites Simoiu, Corbett-Davies and Goel (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Langan (1995)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
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
“Our proposed framework would, therefore, preclude the intentional sabotage of individuals based on protected status by design.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
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
“We propose a method to eliminate bias from predictive models by removing all information regarding protected variables from the data to which the models will ultimately be trained.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The average level of criminality is independent of race.
- Rests on an assumption: In the overwhelming majority of applications, X and Z are dependent.
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
“Ultimately, the better the estimator of the conditional distribution F X j |V (j ) , the closer to fair any prediction rule y estimated on w, so it is critical to construct these estimators with care.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.4. Unknown conditional distributions.
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: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“If the fit is good, this should be close to the uniform distribution on the unit interval.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
hedgedcorrelational- 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 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
“In recidivism prediction, while there likely are interactions between the protected variable and the permitted variables, because the permitted covariates are often selected to be well established, theoretically motivated risk factors, we find this scenario unlikely.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The average level of criminality is independent of race.
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 indicates that a prediction rule δ : x → y is unlikely to be fair for race, and that to guarantee a fair prediction rule we need to estimate and apply a transformation to independence.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
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.
- The authors state this as an assumption: The distribution of risk is independent of race.
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
“Furthermore, standard validation techniques to assess the quality of the model would reveal if this were the case, so long as the high-level evaluators had access to the protected variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
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 indicates that a prediction rule δ : x → y is unlikely to be fair for race, and that to guarantee a fair prediction rule we need to estimate and apply a transformation to independence.”
- Also stated in: § 5. Application: Removing racial dependence in recidivism risk assessment. · hedged
“Put another way, a model which simply excludes race is unlikely to result in fair predictions, as the effect of race will be encapsulated in the estimated effects of each of the variables included in the model.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
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: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: The distribution of risk is independent of race.
- Also stated in: § 5. Application: Removing racial dependence in recidivism risk assessment. · hedged
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
“Thus, fitting models to data for which certain groups are observed committing crime at a disproportionate rate unfairly biases the model's predictions against those groups.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
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
“However, it is insufficient to guarantee that all information about race has been removed from the transformed dataset, as the model may be badly misspecified, e.g., not enough interaction variables were included in the model.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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: passes — The paper gives a 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
“Having established that predictions made using the transformed data are fair under the definition we propose for this context, we now turn to fit assessment.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
boosted- 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.
- The authors state this as an assumption: The distribution of risk is independent of race.
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
“In most cases, Cramer's V is reduced to near zero in the adjusted data, indicating that we have successfully removed information about race from the adjusted data, at least up to two-way interactions.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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: passes — The paper gives a 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
“Thus, it is expected that the predictive accuracy of a model fit to the adjusted data will be lower than the model trained on unadjusted data.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“In any case, the strong dependence of the predicted outcome on race can be viewed as resulting from these differences in rates of re-arrest between the individuals whose status changes after adjustment; these numbers could of course differ at other thresholds.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“Therefore, our procedure need not lead to a deterioration in fairness by all other metrics.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
hedgedSingle case- 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 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 first class of approaches-simply omitting race from the set of covariates used to fit the model-is equally inadequate in this setting, as the covariates that are permitted to be used in the analysis are highly correlated with race.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
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.
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 sufficient to guarantee that we have successfully managed to sample w j as a sample from the marginal distribution of x j for each race category.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“Our second main contribution is that our method allows the user to make adjustments such that the output dataset is mutually independent of the protected variables, as opposed to pairwise independent.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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 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
“Because re-offense is not observed directly, it is very difficult to know the extent to which the observation of re-offense is biased.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could 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 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
“To this end, we implement the procedure described above to remove all information about race from the covariates we will use for prediction, thus guaranteeing similar distributions of estimated risk by race.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
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: unclear — It is unclear why the paper says it holds.
- The authors state this as an assumption: The distribution of risk is independent of race.
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 framework we suggest has extended the existing literature by allowing an arbitrary number of variables of arbitrary type to be both protected and adjusted so long as a suitable conditional model can be found to adequately describe the full conditional distribution of the permitted variables given the protected variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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.
- The authors state this as an assumption: A suitable conditional model can be found to adequately describe the full conditional distribution of the permitted variables given the protected variables.
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
“Given that the outcome variable is observed with bias with respect to the protected variable, we believe that the second set of approaches designed with the objective of achieving equivalent predictive accuracy by race are inappropriate for this particular application, as they ultimately rely upon comparing the model's predictions of re-offense to a fundamentally flawed and biased measure of re-o”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
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.
- Rests on an assumption: The outcome variable (re-arrest) is observed with bias with respect to the protected variable (race).
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
“In addition, if a protected variable is continuous (e.g., protecting parental income in a tool meant to predict success in college for college admissions), simply taking the top p% within each class is infeasible, as classes would have to be made by discretizing, and people who happened to fall on the lower or upper end of each bin would be unfairly disadvantaged.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“Model developers could also apply new machine learning or statistical models for which modifications, such as weights, are difficult or computationally costly to introduce without worry that the models they develop would result in race-dependent predictions.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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: passes — The paper gives a 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
“We have presented a statistical framework for adjusting a dataset such that models trained to the data will be mutually independent of protected variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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. 29
- It may state two separate claims as one. 33
- The quote only partly states this claim. 1
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“This scenario in which there is significant racial bias in re-arrest is specifically that which we seek to address in this paper, and thus the objections of Chouldechova (2017) to demographic parity are not directly relevant to our application.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Could not verifycites Chouldechova (2017) sourceExperimental — accuracy not yet measured
The paper discusses trade-offs between test fairness and equal error rates (Section 2.1, p. 2; Discussion, p. 4), but the passages found do not discuss demographic parity or caution about racial bias in re-arrest. I searched for both terms and for bias in recorded recidivism outcomes. Whether Chouldechova’s analysis is relevant to the citing authors’ application is their own judgment, not a claim the source can verify. (LLM, conf=0.74)
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although Adler et al. (2016) makes some improvements to the handling of categorical variables, the procedure is not appropriate for continuous Z or Z with many distinct values.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Partly supportedcites Adler et al. (2016) sourceExperimental — accuracy not yet measured
In “Computing Influence” (p. 3), the paper says the numerical obscuring procedure requires the feature being removed to be categorical. “Removing numerical features” (p. 3) then describes binning numerical features so the procedure can handle them; the paper does not state that continuous features are categorically inappropriate or identify many distinct values as a limitation. (LLM, conf=0.78)
- Could not verifycites Feldman et al. (2015) sourceExperimental — accuracy not yet measured
In §6 (pp. 16–17), the paper discretizes age into two protected groups and determines repair quantiles using the smallest protected group. Neither that discussion nor §7’s limitations (p. 23) says the procedure is inappropriate when the protected variable is continuous or has many distinct values; the §7 restriction concerns the attributes being repaired. The quoted citing sentence also names Adler et al., rather than Feldman et al. (LLM, conf=0.72)
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.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Kleinberg, Mullainathan and Raghavan (2017) shows theoretically that these notions of fairness are usually incompatible.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Supportedcites Kleinberg, Mullainathan and Raghavan (2017) source
The Introduction (p. 2) describes calibration within groups and balance conditions that generalize equal false-positive and false-negative rates. Theorem 1.1 (Section 1.2, p. 5) shows that all three conditions can hold together only with perfect prediction or equal base rates, supporting the claimed usual incompatibility. (LLM, conf=0.88)
boosted- 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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Under this definition, a model is typically considered fair if differences in the distribution of the model's predictions conditional on the protected variable do not exceed some pre-determined threshold, as measured by some appropriate notion of distance between probability distributions.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Partly supportedcites Feldman et al. (2015) sourceExperimental — accuracy not yet measured
The Introduction (p. 2) defines disparate impact using the probability of a positive outcome conditional on protected status, and Section 4.1 (p. 7) applies a threshold to that measure for a classifier. The paper uses a specific ratio of positive-outcome rates, not a general distance between prediction distributions, so that part of the paraphrase is broader than the source supports. (LLM, conf=0.85)
- Not matchedcites Barocas and Selbst (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Partly supported
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
“This may or may not be unique, but all such random variables are, informally, as close as possible to X while also being independent of Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3. Transformations to independence.
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“The procedure we propose is guaranteed to achieve (3.1) when X is univariate.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3. Transformations to independence.
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.
The quote only partly states this claim.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 contrast, the joint adjustment results in nearly indistinguishable predictive distributions, achieving the goal of the procedure.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 4. Simulation example.
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.
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
“The ordering X 1 , . . ., X p of the X variables is arbitrary, though some orderings may be practically convenient for a given application.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.2. Multivariate adjustments via chaining.
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: 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 joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 pairwise adjustment does reduce some of the discrepancy in the predictive distributions of ŷ | z, but the distribution of ŷ | z = 1 has a much larger right mode than the distribution of ŷ | z = 0.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 4. Simulation example.
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.
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
“Despite achieving W 2 ⊥ ⊥ Z, dependence between W 2 and X 2 is retained. For example, if we select G 2 = Bernoulli(1/2), then cor(W 2 , X 2 ) = 2/3.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.1.2. Atomic example.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- 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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Under this transformation, W 1 | Z ∼ G 1 and, because the distribution is the same for every value of Z, W 1 ⊥ ⊥ Z, so W 1 ∼ G marginally.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.1.1. Continuous example.
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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“This procedure could be applied independently to each covariate X j to achieve pairwise independence with Z, though it is not guaranteed that the resulting set of independently transformed covariates will achieve mutual independence with Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.2. Multivariate adjustments via chaining.
hedgedcorrelational- 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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Though the multivariate procedure does not have the optimality guarantee of the univariate procedure, empirically we find it is very successful at achieving independence with minimal distortion of the distribution of X.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3. Transformations to independence.
boosteddirect- 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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 criminal record variables-juv_misd_count, juv_fel_count, juv_other_count, and priors_count-are highly dispersed counts, and there is evidence of substantial dependence between most pairs of variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- 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.
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 particular, the positive predictive value is reduced by adjustment for Caucasian and Hispanic people and increased for African-Americans.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- 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.
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 mad is unchanged for accuracy, and decreases from 0.05 to 0.02 for fpr.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“On the other hand, adjustment appears to decrease variation by race in false positive rates and only slightly increase variation in accuracy.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 of the logistic regression analysis are qualitatively very similar to those of RF and are deferred to the Appendix.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 unadjusted data, the area under the curve (AUC) was 0.72, and for the adjusted data it was 0.71.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“It is worth noting that the Hispanic group is by far the smallest of the three, so while this group experiences the largest percent increase in high risk classification, the number of individuals whose status changes as a result of adjustment is relatively small.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- 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.
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
“Thus, similar accuracy is achieved by offsetting improvements and degradation of the two metrics, and the direction of the shift differs for African-Americans compared to all others.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 African-Americans, the change in tpp of 0.29 -0.33 = -0.04 is largely offset by the change in tnp of 0.38 -0.35 = 0.03, for a net change of only -0.01 in total accuracy.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“Conversely, the negative predictive value is reduced by adjustment for African-Americans and increased by adjustment for Caucasians and Hispanics.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- 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.
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
“Predictions made by training RF on data adjusted using our procedure eliminate almost all racial disparities, as evidenced by the nearly identical distributions by race in the two panels on the right.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 fpr is decreased for African-Americans and increased for Hispanics and Caucasians.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“When c(x, w) = d q (x, w) for q ≥ 1, the transportation distance is related to the Wasserstein-q distance by W q q (μ, μ) = ρ c (μ, μ), so the optimal coupling-when it exists-is also the coupling achieving the W q distance.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX B: OPTIMAL COUPLING AND TRANSPORT MAPS
plaincorrelational- 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
“Unlike previous work in this area, our procedure accommodates data on any measurement scale.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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 authors state this as an assumption: The average level of criminality is independent of race.
- Rests on an assumption: In the overwhelming majority of applications, X and Z are dependent.
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
“In this setting, a model may very successfully predict that an individual is likely to be re-arrested, but the model has no way of disentangling a higher risk of re-arrest due to elevated latent criminality from higher risk due to increased police attention.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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: passes — The paper gives a 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
“The key to this approach is reliably estimating the ζ j , which in turn requires good choices of G ← and good estimators of F X j |V (j ) .”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.4. Unknown conditional distributions.
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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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 greatly expands the range of datasets that can be transformed and thus expands the universe of problems to which the procedure may be applied.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
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
“If F X j |V (j ) were the exact conditional distribution F X j |V (j ) , then W would satisfy W ⊥ ⊥ Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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 framework also allows us to make transformed data W that are mutually independent of Z, rather than only pairwise independent, thereby guaranteeing that the predictions of any algorithm trained on the transformed data will be independent of Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The average level of criminality is independent of race.
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
“It is typical in applied statistics and regression modeling to transform predictors prior to model fitting for computational reasons or to obtain better predictive accuracy, which would neutralize any choice we make for G.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.3. Defining G.
hedgeddirect- 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.
- The authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“Our approach allows for any number of mixed-scale variables to be adjusted and naturally accommodates one or more protected variables that may be categorical or continuous.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
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.
- The authors state this as an assumption: The average level of criminality is independent of race.
- Rests on an assumption: In the overwhelming majority of applications, X and Z are dependent.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, for a variety of reasons, information on true baseline rates of crime against which to”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
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.
In the paper
“First, it works only for categorical Z, and reasonable performance requires a large number of observations taking each possible level of Z.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
What the sources say
- Not checked yetcites Feldman et al. (2015) 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.
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
“Estimation of F X j |V (j ) is an exercise in regression modeling.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.4. Unknown conditional distributions.
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 authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“In practice, how one chooses or estimates G is less critical than using the conditional CDF to transform X.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 3.3. Defining G.
plain- 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: passes — The paper gives a 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 authors state this as an assumption: The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
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
“In constructing the chain of conditional models, we always include discretized versions w * 1 and w * 2 of w 1 or w 2 , respectively, whenever the w 1 or w 2 are included in the model.This captures nonlinearity in the condi-tional mean of the other variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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: 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.
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
“Of course, F X j |V (j ) is an estimate, and thus it will differ from F X j |V (j ) in finite samples, and even asymptotically when F X j |V (j ) is misspecified.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Methodology for achieving statistical parity has centered on removing information about the protected variables from the training data by transforming the training data.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
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.
In the paper
“In general, we have found that notions of fairness can be divided into three camps.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Not matchedcites Berk (2016)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
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
“If deployed in the real world, the approach we have proposed does not obviate the need for good practices with respect to validation and evaluation.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
plain- 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: passes — The paper gives a 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.
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
“By doing the adjustment, a dataset could be released to multiple organizations to build prediction models, and regardless of the details of their model, we would be guaranteed that the predictions would be fair under the definition we support in this case.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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: unclear — It is unclear why the paper says 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
“Furthermore, this approach limits the risk of malicious actors intentionally biasing the model against protected groups, as model builders would not be able to infer protected characteristics.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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: 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
“In this case, assessment of how well our model predicts Y using any notion of model performance is not especially well motivated, as Y is a biased measure of the phenomenon it is meant to measure.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“The extension to allow for the adjustment of discrete variables is itself an advancement, as previous proposals for adjusting of the training covariates were only designed for adjusting continuous variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
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.
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 nature of dependence among variables is likely to change over time (perhaps, even as a result of the application of such a risk assessment model), so it would be important that these tests are routinely applied to ensure that dependence were adequately removed on an ongoing basis.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
hedgedcorrelational- 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. It may state two separate claims as one.
In the paper
“This procedure is limited to binary outcome data and the adjusted data is not re-usable in the sense that one could not then use the covariates to estimate relationships with other outcome variables and still be ensured of nondiscriminatory outcomes.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Not checked — no full text of the sourcecites Kamiran and Calders (2009) 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.
It may state two separate claims as one.
In the paper
“In this case also, the methods are restricted to binary protected classes and binary outcome variables.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
What the sources say
- Not checked yetcites Calders and Verwer (2010) 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.
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 needs to be done separately for every instantiation of a risk assessment model, as the best mathematical characterization of fairness will likely vary with prediction type and local community understanding of fairness and the goals of implementing the model.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
hedgedcorrelational- 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.
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
“because African Americans are more likely to be re-arrested for re-offending, neither definition seems particularly appropriate for this setting.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
hedgedcorrelational- 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
“Indeed, differences in the marginal rate of arrest could manifest via processes that are entirely unbiased-most obviously, genuine differences in rates of re-offense, though others are possible.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
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.
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
“We expect our procedure would also be of value in data privacy and anonymization.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 6. Discussion.
hedged- 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.
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
“In applying our proposed procedure and the pairwise adjustment, some relevant information is lost.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
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.
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
“because African Americans are more likely to be re-arrested for re-offending, neither definition seems particularly appropriate for this setting.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5. Application: Removing racial dependence in recidivism risk assessment.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could 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.
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 most cases, the allowable difference is zero, which is equivalent to the requirement that the predictive distribution is independent of the protected variable.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
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.
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
“Our paper operates within this definition of fairness and builds upon the extant methodology for mitigating disparate impact.”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > APPENDIX A: OVERVIEW OF NOTIONS OF FAIRNESS
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.
The authors state this as an assumption.
The average level of criminality is independent of race.
“the most reasonable approach is to assume that the average level of criminality is independent of race.”
The authors state this as an assumption.
The joint and conditional distributions of (X, Z) are known, and the target distribution G is fixed and user-specified.
“In the following motivating sections, we assume the joint and conditional distributions of (X, Z) are known, and the desired target distribution, G, is fixed and user-specified.”
The paper relies on this without support.
In the overwhelming majority of applications, X and Z are dependent.
“In the overwhelming majority of applications, X and Z are dependent, and thus we must take additional measures to ensure Y is fair.”
The authors state this as an assumption.
The distribution of risk is independent of race.
“Thus, as we argue above, a reasonable way to proceed is to assume that the distribution of risk is independent of race.”
The paper relies on this without support.
The outcome variable (re-arrest) is observed with bias with respect to the protected variable (race).
“Given that the outcome variable is observed with bias with respect to the protected variable”
The authors state this as an assumption.
A suitable conditional model can be found to adequately describe the full conditional distribution of the permitted variables given the protected variables.
“so long as a suitable conditional model can be found to adequately describe the full conditional distribution of the permitted variables given the protected variables”
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?
In the citing paper
“Applications in perturbationbased data repair [98,125,136,152,154,172] have shown that accuracy is not signiicantly afected.”
From Fairness in machine learning: A survey (2024) · cites this paper as [154]
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
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > Abstract
“random forest or logistic regression fit to only seven transformed variables-mostly pertaining to an individual's criminal history”
§ AN ALGORITHM FOR REMOVING SENSITIVE INFORMATION: APPLICATION TO RACE-INDEPENDENT RECIDIVISM PREDICTION 1 > 5.3. Transformations to independence.
“Of the six x j , one (sex) is binary, one [log(age)henceforth simply "age"] is continuous, and the other four, which relate to prior criminal record, are counts.”
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827
Kusner, M. J., Loftus, J. R., Russell, C., & Silva, R. (2017) · arXiv · doi:10.48550/arxiv.1703.06856 · Cited by 995
Cited by
Sorted by citations
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
“The goal is to transform all predictors so that fair prediction can be obtained “while still preserving as much ‘information’ in X as possible (Johndrow and Lum, 2017: 6).”
BackgroundAlgorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687
Self-citationFairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Towards 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
The Frontiers of Fairness in Machine LearningPreprint
Chouldechova, A., & Roth, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08810 · Cited by 372
Metadata
- Authors
- Johndrow, J. E., & Lum, K.
- Affiliations
- Stanford University (US)
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2019-03-01
- Volume
- 13
- Issue
- 1
- Pages
- 189-220
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- OpenAlex
- W2600853029
- Cited by
- 141
- References
- 40
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 12 · 2023: 14 · 2022: 16 · 2021: 32 · 2020: 28 · 2019: 19
- FWCI
- 41.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- algorithmic bias, sensitive information removal, recidivism prediction, protected characteristics, parole decisions, sentencing, racial disparities, criminal justice
About these checks
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.
A language model drew the claims from the paper on 2026-11-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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