Which term describes the ability to understand and interpret how an AI system makes decisions?

Which term describes the ability to understand and interpret how an AI system makes decisions?

• A. TEVV in AI system development
• B. Explainability in AI
• C. Data quality in AI security
• D. Chairperson

Answer: (B)

Explainability is the ability to understand and interpret how an AI system makes decisions. It involves making the reasoning behind a model’s outputs transparent, so humans can see which features influenced a decision, how those features were weighed, and whether the process can be traced and examined. In governance terms, explainability supports trust, accountability, and oversight. It enables stakeholders to validate that AI decisions align with policies, detect potential bias, and justify outcomes to regulators, auditors, or affected users. Techniques range from inherently interpretable models to post-hoc explanations that shed light on complex systems, including feature importance, surrogate models, and counterfactuals. The other terms don’t describe this capability. TEVV in AI system development isn’t a standard concept focused on understanding decisions; data quality concerns the accuracy and reliability of input data rather than how decisions are made; a chairperson is unrelated to the AI decision-making process.

Explainability is the ability to understand and interpret how an AI system makes decisions. It involves making the reasoning behind a model’s outputs transparent, so humans can see which features influenced a decision, how those features were weighed, and whether the process can be traced and examined.

In governance terms, explainability supports trust, accountability, and oversight. It enables stakeholders to validate that AI decisions align with policies, detect potential bias, and justify outcomes to regulators, auditors, or affected users. Techniques range from inherently interpretable models to post-hoc explanations that shed light on complex systems, including feature importance, surrogate models, and counterfactuals.

The other terms don’t describe this capability. TEVV in AI system development isn’t a standard concept focused on understanding decisions; data quality concerns the accuracy and reliability of input data rather than how decisions are made; a chairperson is unrelated to the AI decision-making process.