mlsanityData to 7 Oct 2026

On conditional parity as a notion of non-discrimination in machine learning

arXiv 1706.08519 ↗First posted 26 Jun 2017stat.ML cs.CY cs.LGcited by 10

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Abstract (arXiv)

We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying randomization as a general mechanism for achieving conditional parity and a kernel-based test of conditional parity.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 2.

10 papers.

  1. 2021Statistical inference for individual fairnesscited by 13
  2. 2021Individually Fair Rankingcited by 7
  3. 2021Measuring Model Fairness under Noisy Covariates: A Theoretical Perspectivecited by 7
  4. 2023Conditional independence testing under misspecified inductive biasescited by 7
  5. 2021Bias, Fairness, and Accountability with AI and ML Algorithmscited by 3
  6. 2022Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Featurescited by 3
  7. 2024Auditing and Enforcing Conditional Fairness via Optimal Transportcited by 1
  8. 2021Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness Notionscited by 0
  9. 2026Differentiable Optimization Layers for Guaranteed Fairness in Deep Learningcited by 0
  10. 2026Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generationcited by 0