Fairness
Summary
Fairness in AI and machine learning is a critical ethical consideration, especially when automated decision-making systems are deployed in high-stakes domains like insurance, lending, hiring, and law enforcement. The concept of fairness aims to prevent or mitigate unfair bias against protected subpopulations based on characteristics such as race, gender, or sexual orientation. One approach to addressing fairness is through the lens of causal inference, as exemplified by the notion of “counterfactual fairness.” This framework posits that a decision is fair if it remains unchanged in both the actual world and a hypothetical world where the individual belongs to a different demographic group. By incorporating such fairness considerations into machine learning models, researchers and practitioners strive to avoid perpetuating or exacerbating discriminatory practices that may be present in historical data, thus working towards more equitable outcomes in AI-driven decision-making processes.