A transparent or "white-box" model is valued because its internal parameters, features, and decision rules can be directly examined. This visibility allows practitioners and stakeholders to understand why the model produced a particular prediction, making it easier to audit for bias and ensure compliance with responsible-AI requirements. Models that instead maximize accuracy through complex, opaque structures, hide parameters, or rely on obfuscation are considered "black-box" approaches and do not provide the same level of interpretability.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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