Lesson plan
Equalized odds
Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rate, satisfying the formula: P(R = + | Y = y, A = a) = P(R = + | Y = y, A = b) \quad y \in \{+,-\} \quad \forall a,b \in A For example, A could be gender, race, or any other characteristics that we want to be free of bias, while Y would be whether the person is qualified for the degree, and the output R would be the school's decision whether to offer the person to study for the degree. In this context, higher university enrollment rates of African Americans compared to whites with similar test scores might be necessary to fulfill the condition of equalized odds, if the "base rate" of Y differs between the groups. The concept was originally defined for binary-valued Y . In 2017, Woodworth et al. generalized the concept further for multiple classes.
Article summary
Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rate, satisfying the formula: P(R = + | Y = y, A = a) = P(R = + | Y = y, A = b) \quad y \in \{+,-\} \quad \forall a,b \in A For example, A could be gender, race, or any other characteristics that we want to be free of bias, while Y would be whether the person is qualified for the degree, and the output R would be the school's decision whether to offer the person to study for the degree. In this context, higher university enrollment rates of African Americans compared to whites with similar test scores might be necessary to fulfill the condition of equalized odds, if the "base rate" of Y differs between the groups. The concept was originally defined for binary-valued Y . In 2017, Woodworth et al. generalized the concept further for multiple classes.
Key claims
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Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
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Key claims and bibliography are generated from the same tracked claims and sources as the article page.