Diverse Counterfactuals

Diverse counterfactual explanations aim to improve the interpretability and fairness of machine learning models by generating multiple alternative inputs that would change a model's prediction. Current research focuses on developing algorithms to generate these counterfactuals, particularly for text and image data, using techniques like inpainting, subgraph generation, and feature-based learning within various model architectures including graph neural networks and large language models. This work is crucial for addressing biases in models, enhancing user trust through explainability, and improving the robustness and fairness of AI systems across diverse applications.