# Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning

arXiv: https://arxiv.org/abs/2505.05471 · HTML: https://mlsanity.com/paper/2505.05471/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 11 of 11)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2211.06684/) (2022)
- [Debiasing Learning for Membership Inference Attacks Against Recommender Systems](https://mlsanity.com/paper/2206.12401/) (2022)
- [Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://mlsanity.com/paper/2306.03527/) (2023)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers](https://mlsanity.com/paper/2301.09210/) (2023)
- [Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems](https://mlsanity.com/paper/2307.00472/) (2023)
- [Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting](https://mlsanity.com/paper/2302.08204/) (2023)
- [Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach](https://mlsanity.com/paper/2308.11912/) (2023)

## Abstract (arXiv)

Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.