As machine learning is increasingly used to inform decision-making in consequential real-world settings (e.g., pre-trial bail, loan approval, or prescribing life-altering medication), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision.

My thesis objective is to study, design, and deploy methods to address the second question, specifically on generating counterfactual explanations and minimal interventions. Thus my focus is on the intersection of machine learning interpretability, causal and probabilistic modelling, and social philosophy and psychology.