Fairness in Machine Learning (FeML)

Fairness in Machine Learning (FeML) involves designing algorithms that make predictions or decisions without biased discrimination against individuals or groups. It aims to ensure that models do not unfairly favor or disadvantage people based on characteristics like race, gender, or age. This is important because biased models can reinforce societal inequalities or lead to unjust outcomes. Fairness techniques seek to identify, measure, and reduce such biases, helping machine learning systems operate more equitably across diverse populations while still providing accurate and reliable results.