Instruction: Propose frameworks or mechanisms to ensure that decisions influenced or made by large language models are ethical, fair, and accountable.
Context: This question delves into the candidate's understanding of ethical AI practices, specifically how to maintain accountability in systems powered by LLMs.
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The way I'd think about it is this: Accountability starts with clear human ownership. If no team is responsible for the model's behavior, then failures become impossible to resolve cleanly. From there, I want logging, versioning, evaluation history, escalation paths, and documentation...