Consequential Decision Making

Consequential decision-making research focuses on improving the fairness, transparency, and explainability of automated systems used in high-stakes domains like finance and healthcare. Current efforts concentrate on developing pre-processing techniques like randomized response to mitigate bias, creating personalized privacy-preserving methods to reduce data requirements, and designing algorithms that generate understandable counterfactual explanations for decisions. This work is crucial for building trust in AI systems and ensuring equitable outcomes, impacting both the development of more responsible AI and the design of fairer societal processes.