Counterfactual Algorithm

Counterfactual algorithms generate hypothetical scenarios ("what-if" analyses) to explain the predictions of complex machine learning models, particularly those deemed "black boxes." Current research focuses on improving the feasibility and plausibility of these explanations, often employing techniques like incorporating data density constraints and sequential patterns, and adapting algorithms for various data types (e.g., time series, images). This work is crucial for enhancing the transparency and trustworthiness of AI systems, particularly in high-stakes applications where understanding model decisions is paramount, and for addressing ethical concerns arising from disagreements among different counterfactual explanations.