paper · First submission: October 24, 2016

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

A formal relationship between predictive parity and error rates clarifies what a fairness claim can mean.

  • Manuscript date: February 28, 2017

Alexandra Chouldechova relates positive predictive value, false-positive rate, false-negative rate and outcome prevalence (2017 manuscript, Eq. 2.6). She pairs the formal analysis with COMPAS data and hypothetical penalty policies.[1]

Read Fairness Tradeoffs for definitions and a worked example, then the investigation entry for the wider reading sequence. Inherent Trade-Offs in the Fair Determination of Risk Scores considers a different combination of score-level requirements.

Historical context

The short manuscript was submitted October 24, 2016; the separate long manuscript appeared February 28, 2017. Both originals backed up; selected definitions, relation and discussion inspected, with rendered long p.7. Versions are not assumed identical. Full proofs, raw records, code and causal sentencing effects were not audited.[1]

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Sources

  1. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments · Source record ref-chouldechova-fair-prediction · Back to claim ↑1 ↑2

Last updated 2026-10-12