Propose a framework for evaluating the effectiveness of AI explainability techniques in a real-world application, considering both quantitative and qualitative metrics.

Instruction: Outline a comprehensive framework that can be used to assess the effectiveness of various AI explainability techniques. Your framework should include a mix of quantitative metrics (such as fidelity or comprehensibility scores) and qualitative assessments (such as user satisfaction or stakeholder feedback). Illustrate how this framework could be applied in a specific real-world scenario of your choosing.

Context: This question is designed to test the candidate's ability to not only understand and apply AI explainability techniques but also to critically evaluate their effectiveness in practical applications. Candidates must demonstrate a deep understanding of both the theoretical aspects of AI explainability and the practical considerations of implementing these techniques in real-world contexts.

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I would evaluate explainability on three levels: technical faithfulness, human usefulness, and governance value. A technique can be mathematically elegant but still fail if users misunderstand it or if it does not help the organization make better decisions...

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