Which action most improves accountability and trust in AI outputs by users?

• A. Increasing explainability
• B. Reducing transparency
• C. Obscuring model internals
• D. Limiting monitoring

Answer: (A)

Explainability is what makes AI decisions understandable and traceable for users. When you can see the reasoning behind a result—the factors considered, data used, and any uncertainties or constraints involved—people can assess whether the outcome is fair, accurate, and appropriate. This visibility lets users verify how the system arrived at a decision, challenge it if needed, and hold the developers and operators responsible for monitoring and improving the model. In short, explainability makes the decision process observable, which directly boosts both accountability and trust.

If transparency is reduced, internals are obscured, or monitoring is limited, the decision path becomes hidden, making it hard to audit, detect bias, or address mistakes. That erosion of visibility undermines trust and weakens accountability.