Add explainability tooling and bias testing so: Which approach best honors the responsible-AI principles in

Explainability tooling plus bias testing satisfies transparency and fairness while preserving performance.

The question

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1. Deploy the opaque model as-is, since accuracy and reliability outweigh transparency and fairness in a fraud context

   Plausible but wrong: prioritizing performance while ignoring mandated explanations and fairness violates responsible-AI balance.
2. Publish the full model architecture publicly, since maximal transparency automatically satisfies fairness obligations too

   Plausible but wrong: disclosing architecture is not applicant-level explanation and does not by itself ensure fairness.
3. Remove all automated decisioning and rely only on manual review, since human-centricity always overrides other principles

   Plausible but wrong: no principle is absolute; discarding a capable model is disproportionate to the stated requirements.
4. Add explainability tooling and bias testing so transparency and fairness are met without abandoning model performance ✓

   Correct: pairing explainability techniques with bias testing satisfies transparency and fairness while retaining performance.

How to remember it

Explainability tooling plus bias testing satisfies transparency and fairness while preserving performance.

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