Interpretability research aims to make AI decision-making transparent, revealing why models produce specific outputs. It is used to debug systems, ensure fairness, and build trust in high-stakes fields like healthcare and finance. Researchers, developers, regulators, and end-users benefit by gaining clarity on AI behavior, reducing risks, and enabling ethical deployment.
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