Bounded explanations are interpretability methods for artificial intelligence systems that provide insights within predefined constraints or scopes. These explanations focus on specific aspects of a model’s behavior, offering clarity on particular decisions or outputs rather than a complete system overview. They are designed to be comprehensible and relevant to a targeted audience or use case. This approach limits the complexity of the explanation to improve its utility.
Context
The application of bounded explanations is gaining traction in regulatory discussions concerning AI transparency and accountability, especially in sensitive sectors. The challenge lies in balancing sufficient detail for understanding with the need for concise, actionable insights. Debates often address how to define appropriate boundaries to ensure both informational value and practical application.
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