# Statistical Equity: A Fairness Classification Objective

arXiv: https://arxiv.org/abs/2005.07293 · HTML: https://mlsanity.com/paper/2005.07293/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 7 of 7)

- [Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation](https://mlsanity.com/paper/2101.04108/) (2021)
- [Large Language Models Still Exhibit Bias in Long Text](https://mlsanity.com/paper/2410.17519/) (2024)
- [Attributing Fair Decisions with Attention Interventions](https://mlsanity.com/paper/2109.03952/) (2021)
- [Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions](https://mlsanity.com/paper/2202.03734/) (2022)
- [Retiring DeltaDP: New Distribution-Level Metrics for Demographic Parity](https://mlsanity.com/paper/2301.13443/) (2023)
- [The Equity Framework: Fairness Beyond Equalized Predictive Outcomes](https://mlsanity.com/paper/2205.01072/) (2022)
- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)

## Abstract (arXiv)

Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair." Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.