Fairness Metrics: Demographic Parity, Equalized Odds, and Trade-offs › Fairness Metrics

When a hiring algorithm rejects qualified candidates from certain demographic groups at a higher rate, is that unfair? What if a medical risk model under-predicts illness severity for one population, even when its overall accuracy looks fine? These questions cannot be answered by accuracy alone. They require a vocabulary of fairness: a set of mathematical definitions that let you precisely specify what it means for a model to treat different groups equitably.

The field of algorithmic fairness emerged as a research discipline in the mid-2010s, driven by a wave of high-profile cases where automated decision systems were found to systematically disadvantage certain populations. Audits of recidivism risk tools in the criminal justice system revealed that Black defendants were assigned higher risk scores at rates inconsistent with their actual recidivism outcomes relative to white defendants. Facial recognition systems showed substantially higher error rates for darker-skinned women compared to lighter-skinned men. Hiring algorithms trained on historical data learned to penalize features correlated with being a woman, not because the features were causally related to job performance, but because the training data reflected past discriminatory hiring practices. These cases drew attention to the gap between a model's aggregate accuracy and its equitable treatment of individuals.

Researchers responded by developing formal definitions of fairness that could be measured, compared, and optimized, much like accuracy or precision. The resulting literature is rich and sometimes confusing: there are dozens of distinct fairness metrics with different names in different papers, many of which measure related but not identical things. This chapter cuts through that complexity by organizing the major definitions into coherent families and explaining the precise relationships between them.

Fairness metrics formalize intuitions about equitable treatment into measurable quantities. They give you a way to ask: does my model perform equally well across demographic groups? Does it fail in systematically different ways for different populations? Are individuals with similar characteristics treated similarly? The answers to these questions depend heavily on which fairness definition you adopt, because the definitions are not equivalent, and in most real-world settings they cannot all be satisfied at once.

This chapter covers the major families of fairness metrics: group fairness criteria like demographic parity and equalized odds, individual fairness and counterfactual approaches, calibration as a fairness requirement, and the fundamental mathematical tensions between them. By the end, you will understand how to calculate these metrics and why choosing between them requires thinking carefully about what kind of fairness matters most in your specific context.

Building on the bias measurement techniques from the previous chapter and the mitigation strategies explored in Bias Mitigation, fairness metrics are the formal evaluation layer that lets you verify whether your interventions achieve fairness, and according to which definition. Where bias measurement asks "does my model exhibit disparities?", fairness metrics provide the precise mathematical language to say "which kind of disparities, how large, and what would it mean to eliminate them."