equal opportunity
In plain wordsA fairness test that asks whether people who truly deserve a yes (who would repay the loan or succeed in the job) get one at the same rate in every group.
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Machine learning
A relaxation of equalized oddsequalized odds, n.: A fairness test that asks whether a system makes mistakes at the same rates for every group: wrongly flagging innocent people, and wrongly missing real cases, equally often. that requires only equal true positive rates across groups: among individuals whose true outcome is favorable (Y = 1), the probability of a positive prediction must not depend on the protected attributeprotected attribute, n.: A personal trait, such as race, sex, religion, age or disability, that the law says must not be used to treat people unfairly.. It suits settings where the fairness concern is wrongly denying qualified people (loans, admissions, hiring) rather than wrongly granting. Like equalized odds, it can be achieved by post-processing with group-specific thresholds, and it is only as fair as the outcome labels it relies on.
Under equal opportunity, creditworthy applicants from both neighborhoods must be approved at the same rate.
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Law and policy
The broader political and legal ideal that people should compete for jobs, education and other positions on equal terms, free of discrimination, as in US equal employment opportunity law. The machine-learning criterion borrows the name and formalizes one narrow reading of it.
Origin
The technical criterion was introduced by Moritz Hardt, Eric Price and Nathan Srebro (2016) alongside equalized odds, borrowing the name of the long-standing civil-rights ideal embodied, for example, in the US Equal Employment Opportunity Commission (established 1965).
Related
- equalized oddsequalized odds, n.: A fairness test that asks whether a system makes mistakes at the same rates for every group: wrongly flagging innocent people, and wrongly missing real cases, equally often.
- demographic paritydemographic parity, n.: A fairness test that asks whether a system says yes (approves the loan, shortlists the applicant) at the same rate for every group, whatever other differences exist between them.
- group fairnessgroup fairness, n.: Measuring fairness by comparing how a system treats whole groups, such as women and men, on average: their approval rates, their error rates or what their scores mean.
- disparate impactdisparate impact, n.: When a rule or system that treats everyone alike on paper still lands much harder on one group, such as women or older workers; the law can forbid this even if no one intended it.
- protected attributeprotected attribute, n.: A personal trait, such as race, sex, religion, age or disability, that the law says must not be used to treat people unfairly.
Sources
- Hardt, M., Price, E. & Srebro, N. (2016). Equality of Opportunity in Supervised Learning. Advances in Neural Information Processing Systems 29. arxiv.org/abs/1610.02413
Mentioned in
algorithmic fairnessalgorithmic fairness, n.: The effort to make automated decisions fair: to say in numbers what fair treatment means, measure whether a system achieves it, and correct the system if it does not., demographic paritydemographic parity, n.: A fairness test that asks whether a system says yes (approves the loan, shortlists the applicant) at the same rate for every group, whatever other differences exist between them., equalized oddsequalized odds, n.: A fairness test that asks whether a system makes mistakes at the same rates for every group: wrongly flagging innocent people, and wrongly missing real cases, equally often., group fairnessgroup fairness, n.: Measuring fairness by comparing how a system treats whole groups, such as women and men, on average: their approval rates, their error rates or what their scores mean.