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

arXiv: https://arxiv.org/abs/1706.08519 · HTML: https://mlsanity.com/paper/1706.08519/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2017.

## Built on by (most cited first, 10 of 10)

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- [Individually Fair Ranking](https://mlsanity.com/paper/2103.11023/) (2021)
- [Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective](https://mlsanity.com/paper/2105.09985/) (2021)
- [Conditional independence testing under misspecified inductive biases](https://mlsanity.com/paper/2307.02520/) (2023)
- [Bias, Fairness, and Accountability with AI and ML Algorithms](https://mlsanity.com/paper/2105.06558/) (2021)
- [Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features](https://mlsanity.com/paper/2202.11499/) (2022)
- [Auditing and Enforcing Conditional Fairness via Optimal Transport](https://mlsanity.com/paper/2410.14029/) (2024)
- [Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness Notions](https://mlsanity.com/paper/2107.01277/) (2021)
- [Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning](https://mlsanity.com/paper/2605.17118/) (2026)
- [Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation](https://mlsanity.com/paper/2608.02551/) (2026)

## 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.