Counterfactual Reasoning for Responsible AI Assessment

As the use of AI and ML models continues to grow, concerns about potential unfairness have become more prominent. Many researchers have focused on developing new definitions of fairness or identifying biased predictions, but these approaches have limited scope and fail to analyze the minimum changes in user characteristics required for positive outcomes (i.e. counterfactuals). In response, this proposed methodology aims to use counterfactual reasoning to identify unfair behaviours in the case of fairness under unawareness. Furthermore, counterfactual reasoning can serve as a comprehensive methodology for evaluating all the essential conditions for a reliable, responsible, and trustworthy model.