Semantic interpretability refers to the ability to understand and explain the decisions or internal workings of an artificial intelligence model in terms of human-understandable concepts and meanings. It moves beyond simply identifying contributing features to providing explanations that align with human reasoning and domain knowledge. This characteristic allows users to comprehend why a model arrived at a particular conclusion. It focuses on meaningful, context-rich explanations.
Context
Achieving semantic interpretability is a significant challenge and an active area of research in artificial intelligence, particularly for complex models used in critical applications. Discussions frequently address the need for AI systems to offer transparent and justifiable outcomes to build user trust and meet regulatory requirements. The development of methods that provide clearer, more intuitive explanations remains a key objective.
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