Bias Detection and Correction Methods for Machine Learning Algorithms

Machine learning algorithms are powerful tools for solving complex problems and making data-driven decisions. However, they can also inherit or amplify biases from the data, the design, or the deployment of the algorithms, leading to unfair or discriminatory outcomes for certain groups or individuals. In this paper, we review the sources, types, and measures of bias in machine learning algorithms, and survey the existing methods for detecting and correcting bias at different stages of the algorithmic pipeline. We also discuss the challenges and limitations of these methods, and highlight some open questions and future directions for research on bias in machine learning algorithms.