State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers
It tackles fairness issues in ML for society, but is incremental as it synthesizes existing knowledge without new empirical results.
This work addresses the problem of unfair and discriminatory decisions made by machine learning algorithms, such as in credit lending, by providing an introduction to discrimination, legislative foundations like GDPR, and strategies for detection and prevention.
Machine learning is becoming an ever present part in our lives as many decisions, e.g. to lend a credit, are no longer made by humans but by machine learning algorithms. However those decisions are often unfair and discriminating individuals belonging to protected groups based on race or gender. With the recent General Data Protection Regulation (GDPR) coming into effect, new awareness has been raised for such issues and with computer scientists having such a large impact on peoples lives it is necessary that actions are taken to discover and prevent discrimination. This work aims to give an introduction into discrimination, legislative foundations to counter it and strategies to detect and prevent machine learning algorithms from showing such behavior.