Explanation Plausibility

Explanation plausibility in artificial intelligence focuses on assessing how believable and understandable AI-generated explanations are to humans, aiming to improve trust and transparency in AI systems. Current research investigates this across various domains, employing methods like generative adversarial networks, normalizing flows, and contrastive learning to generate or evaluate explanations, often within the context of specific model architectures such as transformers. This research is crucial for enhancing the usability and acceptance of AI, particularly in high-stakes applications where understanding AI decision-making is paramount, and for developing more robust evaluation metrics for explainable AI methods. The ultimate goal is to create AI systems that not only perform well but also provide explanations that are both accurate and easily grasped by users.