What term describes the equitable treatment of individuals across demographic groups in AI systems to prevent unfair outcomes?

What term describes the equitable treatment of individuals across demographic groups in AI systems to prevent unfair outcomes?

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

Answer: (A)

Equitable treatment across demographic groups in AI systems is called fairness in AI. This term captures the goal of preventing unfair outcomes by ensuring decisions don’t systematically disadvantage people based on sensitive attributes like race, gender, or age. In practice, fairness can be pursued through different formal criteria, such as demographic parity (equal decision rates across groups), equalized odds (equal error rates across groups), or individual fairness (similar individuals receive similar outcomes). The focus is on reducing bias in automated decisions, whether in hiring, lending, or risk assessment, so that protected characteristics don’t lead to unfair results. Other terms describe different concerns: transparency is about making how the model works understandable; bias in AI refers to the presence of prejudice in data or models; privacy in AI centers on protecting personal information and preventing unauthorized data use.

Equitable treatment across demographic groups in AI systems is called fairness in AI. This term captures the goal of preventing unfair outcomes by ensuring decisions don’t systematically disadvantage people based on sensitive attributes like race, gender, or age. In practice, fairness can be pursued through different formal criteria, such as demographic parity (equal decision rates across groups), equalized odds (equal error rates across groups), or individual fairness (similar individuals receive similar outcomes). The focus is on reducing bias in automated decisions, whether in hiring, lending, or risk assessment, so that protected characteristics don’t lead to unfair results.

Other terms describe different concerns: transparency is about making how the model works understandable; bias in AI refers to the presence of prejudice in data or models; privacy in AI centers on protecting personal information and preventing unauthorized data use.