# Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity

arXiv: https://arxiv.org/abs/2204.05947 · HTML: https://mlsanity.com/paper/2204.05947/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [Stability and Multigroup Fairness in Ranking with Uncertain Predictions](https://mlsanity.com/paper/2402.09326/) (2024)
- [Critical Appraisal of Fairness Metrics in Clinical Predictive AI](https://mlsanity.com/paper/2506.17035/) (2025)
- [AI Biases as Asymmetries: A Review to Guide Practice](https://mlsanity.com/paper/2503.07326/) (2025)

## Abstract (arXiv)

Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected outcomes regardless of group. Testing and satisfying PP is especially important in many settings where model scores are interpreted by humans or directly provide access to opportunity, such as healthcare or banking. Solutions for PP violations have primarily been studied through the lens of model calibration. However, we find that existing calibration-based tests and mitigation methods are designed for independent data, which is often not assumable in large-scale applications such as social media or medical testing. In this work, we address this issue by developing a statistically rigorous non-parametric regression based test for PP with dependent observations. We then apply our test to illustrate that PP testing can significantly vary under the two assumptions. Lastly, we provide a mitigation solution to provide a minimally-biased post-processing transformation function to achieve PP.