What are fairness metrics?

What are fairness metrics?

• A. Quantitative measures used to assess fairness, often requiring explicit choices/tradeoffs.
• B. Subjective judgments by analysts about fairness.
• C. Legal standards for nondiscrimination applied to models.
• D. Data quality metrics that ignore fairness considerations.

Answer: (A)

Fairness metrics are numerical measures used to assess how fair a model’s outcomes are across different groups defined by protected attributes. They turn a concept like fairness into something you can compute and compare, by selecting a specific criterion for what counts as fair and then evaluating the model against it. Because there isn’t a single universal notion of fairness, you must choose which criterion to pursue—for example, equal decision rates across groups, equal error rates, or similar calibration within groups. That choice inherently creates tradeoffs with other goals, typically with overall accuracy or utility, so improving one aspect of fairness can come at the cost of another performance metric. This is why fairness metrics are deliberately explicit about what they measure and how, enabling transparent comparisons and adjustments. They’re not just subjective judgments from analysts, which would be qualitative impressions rather than measured quantities. They aren’t legal standards by themselves, though they help organizations assess compliance with nondiscrimination laws. And they aren’t general data quality metrics that ignore fairness; they specifically quantify disparities and equity concerns across groups.

Fairness metrics are numerical measures used to assess how fair a model’s outcomes are across different groups defined by protected attributes. They turn a concept like fairness into something you can compute and compare, by selecting a specific criterion for what counts as fair and then evaluating the model against it. Because there isn’t a single universal notion of fairness, you must choose which criterion to pursue—for example, equal decision rates across groups, equal error rates, or similar calibration within groups. That choice inherently creates tradeoffs with other goals, typically with overall accuracy or utility, so improving one aspect of fairness can come at the cost of another performance metric. This is why fairness metrics are deliberately explicit about what they measure and how, enabling transparent comparisons and adjustments.

They’re not just subjective judgments from analysts, which would be qualitative impressions rather than measured quantities. They aren’t legal standards by themselves, though they help organizations assess compliance with nondiscrimination laws. And they aren’t general data quality metrics that ignore fairness; they specifically quantify disparities and equity concerns across groups.