Bias Detection and Fairness Optimization in Machine Learning Algorithms

Abstract

There are serious worries about prejudice and unfairness with the growing use of machine learning algorithms in decision-making systems. This is especially true in politically charged areas like healthcare, banking, hiring, and law enforcement. Machine learning models can be biased due to historical or imbalanced datasets, poor feature selection, or algorithmic design decisions. This bias can result in discriminating outcomes, which can worsen socioeconomic disparities. examines current methods for detecting bias and evaluating fairness, as well as the kinds and origins of bias in machine learning algorithms. Methods for reducing bias in models without sacrificing performance during pre-, in-, and post-processing. To evaluate the behaviour of algorithms, bias detection techniques are examined, including disparate effect analysis, statistical parity difference, and fairness metrics across protected attributes. In this article, we take a look at the pros and downsides of several fairness optimization methods, such as data re-sampling, adversarial debiasing, and constraint-based learning.