Fairness Metrics
Quantitative measures used to evaluate whether AI systems treat different groups equitably across protected characteristics.
Definition
Fairness metrics include statistical measures like demographic parity, equalized odds, and individual fairness that assess whether AI systems produce biased outcomes across race, gender, age, or other protected attributes.
These metrics help organizations identify, measure, and mitigate discriminatory patterns in AI decision-making, though different fairness definitions may conflict and require careful consideration of business context.
Why It Matters
Implementing fairness metrics helps businesses avoid discriminatory AI practices that could result in legal liability, regulatory penalties, and reputational damage while ensuring equitable customer treatment.
Fairness measurement enables proactive bias mitigation and demonstrates commitment to ethical AI practices, building stakeholder trust and supporting sustainable business growth in diverse markets.
Examples in Practice
Hiring platforms measure demographic parity in resume screening algorithms to ensure equal opportunity across different ethnic and gender groups while maintaining prediction accuracy for job performance.
Lending institutions evaluate equalized odds in credit scoring models to verify that loan approval rates are fair across protected classes while preserving the ability to assess credit risk accurately.
Healthcare AI systems monitor fairness metrics in diagnostic tools to ensure equitable treatment recommendations across different patient demographics, maintaining clinical effectiveness while reducing health disparities.