Which term best matches the goal of ensuring AI decisions do not systematically disadvantage any group?

Which term best matches the goal of ensuring AI decisions do not systematically disadvantage any group?

• A. Fairness in AI
• B. Transparency in AI
• C. Bias in AI
• D. Privacy in AI

Answer: (A)

The main idea here is fairness in AI. It focuses on ensuring that AI decisions don’t produce systematic disadvantages for any group, especially groups protected by law or ethics (such as race, gender, or age). In practice, this means designing and evaluating models so that outcomes are equitable across different groups, not biased toward or against any one group. This involves checking for disparities in decision results and, when needed, applying methods to reduce those gaps while maintaining overall usefulness of the model. For example, if two applicants with similar qualifications are treated differently because of a protected attribute, fairness work would aim to adjust the model or data to remove that bias. Transparency is about how decisions are made and explained; it helps people understand the model but doesn’t automatically ensure equal outcomes. Bias in AI refers to the presence of prejudice in data or model behavior that leads to unfair results; fairness is the goal that guides how we address and reduce such bias. Privacy in AI focuses on protecting individuals’ personal data and sensitive information, not on the distribution of outcomes across groups.

The main idea here is fairness in AI. It focuses on ensuring that AI decisions don’t produce systematic disadvantages for any group, especially groups protected by law or ethics (such as race, gender, or age). In practice, this means designing and evaluating models so that outcomes are equitable across different groups, not biased toward or against any one group. This involves checking for disparities in decision results and, when needed, applying methods to reduce those gaps while maintaining overall usefulness of the model. For example, if two applicants with similar qualifications are treated differently because of a protected attribute, fairness work would aim to adjust the model or data to remove that bias.

Transparency is about how decisions are made and explained; it helps people understand the model but doesn’t automatically ensure equal outcomes. Bias in AI refers to the presence of prejudice in data or model behavior that leads to unfair results; fairness is the goal that guides how we address and reduce such bias. Privacy in AI focuses on protecting individuals’ personal data and sensitive information, not on the distribution of outcomes across groups.