Which objective is addressed by auditing AI decisions for fairness and validity?

• A. Transparency and fairness in decision making
• B. Hardware compatibility checks
• C. Data archival procedures
• D. Licensing compliance only

Answer: (A)

Auditing AI decisions for fairness and validity aims to make how a model decides both understandable and trustworthy. Transparency comes from revealing why a decision was made, including the inputs used, the factors the model considered, and the reasoning path, so stakeholders can see that the outcome isn’t a mysterious black box. Fairness focuses on whether the decisions are unbiased and equitable across different groups, checking for discriminatory patterns and applying fairness metrics to prevent unequal treatment. Validity ensures the outcomes align with the intended purpose, data quality, and domain rules—confirming that the model isn’t producing results that are inconsistent, nonsensical, or outside what the system is designed to handle.

This combination supports governance and risk management by promoting accountability and enabling oversight of AI behavior, which is essential for trust, regulatory compliance, and responsible deployment.

Other options address different objectives: hardware compatibility is about whether the system runs on the right hardware, data archival procedures concern retention and retrieval of data, and licensing compliance relates to legal use of software.