Global Counterfactual

Global counterfactual explanations aim to provide high-level, model-agnostic understandings of complex machine learning models, moving beyond individual predictions to reveal systemic biases and overall model behavior. Current research focuses on developing efficient algorithms, often leveraging reinforcement learning, graph neural networks, or tree-based methods, to generate concise and interpretable global summaries of counterfactual scenarios, incorporating human-defined principles where possible. This work is crucial for enhancing the transparency and accountability of AI systems, particularly in high-stakes domains like medicine and finance, by providing insights into model decision-making processes at a broader scale.