Which term refers to safeguards designed to prevent discriminative outcomes in AI predictions?

Which term refers to safeguards designed to prevent discriminative outcomes in AI predictions?

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

Answer: (A)

Fairness in AI focuses on safeguarding against discriminative outcomes by ensuring predictions don’t systematically disadvantage people or groups based on sensitive attributes like race, gender, or age. It treats equitable treatment as a design goal and uses methods such as fairness metrics (checking for similar error or positive decision rates across groups), data debiasing, and model constraints to meet fairness criteria. Auditing for disparate impact and adjusting data or algorithms helps prevent biased outcomes while keeping predictive performance reasonable. By contrast, bias in AI refers to the presence of prejudice or distorted data or models that can cause unfair results, transparency in AI is about making how decisions are made understandable, and privacy in AI is about protecting individuals’ data. So the safeguards aimed at preventing discriminatory outcomes are described as fairness in AI.

Fairness in AI focuses on safeguarding against discriminative outcomes by ensuring predictions don’t systematically disadvantage people or groups based on sensitive attributes like race, gender, or age. It treats equitable treatment as a design goal and uses methods such as fairness metrics (checking for similar error or positive decision rates across groups), data debiasing, and model constraints to meet fairness criteria. Auditing for disparate impact and adjusting data or algorithms helps prevent biased outcomes while keeping predictive performance reasonable. By contrast, bias in AI refers to the presence of prejudice or distorted data or models that can cause unfair results, transparency in AI is about making how decisions are made understandable, and privacy in AI is about protecting individuals’ data. So the safeguards aimed at preventing discriminatory outcomes are described as fairness in AI.