# Group Fairness: Independence Revisited

arXiv: https://arxiv.org/abs/2101.02968 · HTML: https://mlsanity.com/paper/2101.02968/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2021.

## Built on by (most cited first, 21 of 21)

- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Fairness Reprogramming](https://mlsanity.com/paper/2209.10222/) (2022)
- [Gradual (In)Compatibility of Fairness Criteria](https://mlsanity.com/paper/2109.04399/) (2021)
- [Metrizing Fairness](https://mlsanity.com/paper/2205.15049/) (2022)
- [Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features](https://mlsanity.com/paper/2202.11499/) (2022)
- [A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs](https://mlsanity.com/paper/2206.02891/) (2022)
- [Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems](https://mlsanity.com/paper/2307.00472/) (2023)
- [Fair Models in Credit: Intersectional Discrimination and the Amplification of Inequity](https://mlsanity.com/paper/2308.02680/) (2023)
- [Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective](https://mlsanity.com/paper/2506.19028/) (2025)
- [Insights From Insurance for Fair Machine Learning](https://mlsanity.com/paper/2306.14624/) (2023)
- [Authorship and the Politics and Ethics of LLM Watermarks](https://mlsanity.com/paper/2403.06593/) (2024)
- [Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies](https://mlsanity.com/paper/2505.22829/) (2025)
- [Fairness and Randomness in Machine Learning: Statistical Independence and Relativization](https://mlsanity.com/paper/2207.13596/) (2022)
- [A multidomain relational framework to guide institutional AI research and adoption](https://mlsanity.com/paper/2303.10106/) (2023)
- [Reliability Gaps Between Groups in COMPAS Dataset](https://mlsanity.com/paper/2308.15243/) (2023)
- [Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights](https://mlsanity.com/paper/2502.19463/) (2025)
- [A Systematic Approach to Group Fairness in Automated Decision Making](https://mlsanity.com/paper/2109.04230/) (2021)
- [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025)
- [Simple and Effective Specialized Representations for Fair Classifiers](https://mlsanity.com/paper/2505.11740/) (2025)
- [Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System](https://mlsanity.com/paper/2507.08864/) (2025)
- [Transparent and Fair Profiling in Employment Services: Evidence from Switzerland](https://mlsanity.com/paper/2509.11847/) (2025)

## Abstract (arXiv)

This paper critically examines arguments against independence, a measure of group fairness also known as statistical parity and as demographic parity. In recent discussions of fairness in computer science, some have maintained that independence is not a suitable measure of group fairness. This position is at least partially based on two influential papers (Dwork et al., 2012, Hardt et al., 2016) that provide arguments against independence. We revisit these arguments, and we find that the case against independence is rather weak. We also give arguments in favor of independence, showing that it plays a distinctive role in considerations of fairness. Finally, we discuss how to balance different fairness considerations.