# Causal foundations of bias, disparity and fairness

arXiv: https://arxiv.org/abs/2207.13665 · HTML: https://mlsanity.com/paper/2207.13665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 89 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Path-Specific Counterfactual Fairness](https://mlsanity.com/paper/1802.08139/) (2018)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [Causal Fairness Analysis](https://mlsanity.com/paper/2207.11385/) (2022)
- [Historical comparison of gender inequality in scientific careers across countries and disciplines](https://mlsanity.com/paper/1907.04103/) (2019)
- [The extent and drivers of gender imbalance in neuroscience reference lists](https://mlsanity.com/paper/2001.01002/) (2020)
- [Fairness criteria through the lens of directed acyclic graphical models](https://mlsanity.com/paper/1906.11333/) (2019)
- [Citation inequity and gendered citation practices in contemporary physics](https://mlsanity.com/paper/2112.09047/) (2021)
- [Men Set Their Own Cites High: Gender and Self-citation across Fields and over Time](https://mlsanity.com/paper/1607.00376/) (2016)
- [The academic motherload: Models of parenting engagement and the effect on academic productivity and performance](https://mlsanity.com/paper/2108.05376/) (2021)

## Abstract (arXiv)

The study of biases, such as gender or racial biases, is an important topic in the social and behavioural sciences. However, the literature does not always clearly define the concept. Definitions of bias are often ambiguous or not provided at all. To study biases in a precise manner, it is important to have a well-defined concept of bias. We propose to define bias as a direct causal effect that is unjustified. We propose to define the closely related concept of disparity as a direct or indirect causal effect that includes a bias. Our proposed definitions can be used to study biases and disparities in a more rigorous and systematic way. We compare our definitions of bias and disparity with various criteria of fairness introduced in the artificial intelligence literature. In addition, we discuss how our definitions relate to discrimination. We illustrate our definitions of bias and disparity in two case studies, focusing on gender bias in science and racial bias in police shootings. Our proposed definitions aim to contribute to a better appreciation of the causal intricacies of studies of biases and disparities. We hope that this will also promote an improved understanding of the policy implications of such studies.