AWS outlines its approach to the responsible use of machine learning (ML) across three phases: design, deployment, and operation. Key practices include evaluating use case risks, ensuring diverse development teams, and minimizing bias in data and models. AWS emphasizes transparency, human oversight, legal compliance, and continuous feedback. Tools like SageMaker Clarify, A2I, and Model Monitor support fairness and explainability. Ongoing testing, education, and governance frameworks help organizations maintain accountability and adapt to evolving technology and societal standards.

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