Which outcome is associated with transparency and explainability in AI systems?

• A. Building trust and accountability
• B. Reducing data usage
• C. Eliminating regulation
• D. Increasing model complexity without benefits

Answer: (A)

Transparency and explainability in AI drive trust and accountability by making how decisions are reached visible to users, developers, and regulators. When you can see which data and features influenced an outcome and follow the reasoning behind a decision, you can assess fairness, detect biases, and verify results. This clarity enables auditing, challenge, and governance, so organizations can be held responsible for model behavior and can correct issues promptly. Reducing data usage, eliminating regulation, or increasing model complexity without benefits are not outcomes associated with transparency; the aim of explainability is to provide clear, understandable reasoning and justification, not to hide or complicate the model.