Designing Fairness-Aware Machine Learning Algorithms: Techniques and Challenges

Abstract

Machine learning (ML) algorithms have seen widespread adoption across various sectors, often driving significant societal impacts. However, these algorithms can perpetuate or even exacerbate existing biases, leading to unfair outcomes for certain groups. Fairness-aware machine learning seeks to address these issues by developing algorithms that ensure equitable treatment across diverse populations. This paper provides an in-depth analysis of fairness-aware machine learning algorithms, exploring their definitions, methodologies, and applications, as well as the challenges and future directions in the field.