Counterfactual Situation Testing

Counterfactual situation testing (CST) is a causal data mining technique used to identify and explain discriminatory outcomes in machine learning models, particularly in high-stakes applications like loan approvals or hiring processes. Current research focuses on developing CST frameworks that leverage counterfactual reasoning to assess model fairness by comparing outcomes for individuals with altered protected attributes (e.g., race, gender), revealing biases even when models superficially appear fair. This approach is proving valuable for debugging biased models, improving model transparency, and ultimately promoting fairer and more equitable algorithmic decision-making across various domains.