Bias Mitigation Strategies in Machine Learning Algorithms

Abstract

Bias in machine learning algorithms poses significant ethical and practical challenges, influencing decisions in areas ranging from finance to criminal justice. This paper examines various strategies to mitigate bias in these algorithms. We review both technical approaches and broader methodological considerations, highlighting their effectiveness, limitations, and ethical implications.