Explanation Evaluation

Explanation evaluation in artificial intelligence focuses on assessing the quality and usefulness of explanations generated by complex models, aiming to bridge the gap between model transparency and human understanding. Current research emphasizes developing robust evaluation frameworks that move beyond simple proxy metrics, incorporating factors like faithfulness, intelligibility, and impact on human decision-making, often utilizing techniques like counterfactual analysis and algorithmic simulations to complement human studies. This work is crucial for building trust in AI systems across diverse applications, from medical diagnosis to legal reasoning, by ensuring that explanations are not only accurate but also effectively aid human users.