Quantifying fairness and discrimination in predictive models

TL;DR

This paper reviews the concepts of fairness and discrimination in predictive models, discussing group and individual fairness, and explores methods to mitigate bias for more ethical machine learning applications.

Contribution

It provides a comprehensive overview of fairness definitions in classification models and discusses approaches to correct discrimination, emphasizing ethical considerations in machine learning.

Findings

Fairness can be defined through independence between sensitive variables and predictions.

Group fairness and individual fairness are key concepts in assessing discrimination.

Methods exist to correct biases and improve ethical standards in predictive models.

Abstract

The analysis of discrimination has long interested economists and lawyers. In recent years, the literature in computer science and machine learning has become interested in the subject, offering an interesting re-reading of the topic. These questions are the consequences of numerous criticisms of algorithms used to translate texts or to identify people in images. With the arrival of massive data, and the use of increasingly opaque algorithms, it is not surprising to have discriminatory algorithms, because it has become easy to have a proxy of a sensitive variable, by enriching the data indefinitely. According to Kranzberg (1986), "technology is neither good nor bad, nor is it neutral", and therefore, "machine learning won't give you anything like gender neutrality `for free' that you didn't explicitely ask for", as claimed by Kearns et a. (2019). In this article, we will come back to the general context, for predictive models in classification. We will present the main concepts of fairness, called group fairness, based on independence between the sensitive variable and the prediction, possibly conditioned on this or that information. We will finish by going further, by presenting the concepts of individual fairness. Finally, we will see how to correct a potential discrimination, in order to guarantee that a model is more ethical