# Towards a Measure of Individual Fairness for Deep Learning

arXiv: https://arxiv.org/abs/2009.13650 · HTML: https://mlsanity.com/paper/2009.13650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

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

- [Measuring Fairness of Text Classifiers via Prediction Sensitivity](https://mlsanity.com/paper/2203.08670/) (2022)
- [Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks](https://mlsanity.com/paper/2110.04397/) (2021)

## Abstract (arXiv)

Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions. We propose a novel measure of individual fairness, called prediction sensitivity, that approximates the extent to which a particular prediction is dependent on a protected attribute. We show how to compute prediction sensitivity using standard automatic differentiation capabilities present in modern deep learning frameworks, and present preliminary empirical results suggesting that prediction sensitivity may be effective for measuring bias in individual predictions.