Fairness in Machine Learning (FML)

Fairness in Machine Learning (FML) involves designing algorithms that make decisions without unfair bias or discrimination against individuals or groups. It aims to ensure that outcomes—such as loan approvals, hiring decisions, or medical diagnoses—are equitable across different demographics like race, gender, or age. Achieving fairness helps prevent algorithms from unintentionally perpetuating societal inequalities, promoting trust and ethical use of technology. It’s about making sure machines treat everyone fairly based on relevant information, not prejudiced assumptions or stereotypes.