# Assessing Gender Bias in Predictive Algorithms using eXplainable AI

arXiv: https://arxiv.org/abs/2203.10264 · HTML: https://mlsanity.com/paper/2203.10264/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Bias and Discrimination in AI: a cross-disciplinary perspective](https://mlsanity.com/paper/2008.07309/) (2020)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [Modelling GDPR-Compliant Explanations for Trustworthy AI](https://mlsanity.com/paper/2109.04165/) (2021)

## Built on by (most cited first, 2 of 2)

- [Automatic Discovery of Visual Circuits](https://mlsanity.com/paper/2404.14349/) (2024)
- [Towards Interpretability in Audio and Visual Affective Machine Learning: A Review](https://mlsanity.com/paper/2306.08933/) (2023)

## Abstract (arXiv)

Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and discuss its implications.