Enforcing Delayed-Impact Fairness Guarantees

• URL: http://arxiv.org/abs/2208.11744v1
• Date: Wed, 24 Aug 2022 19:14:56 GMT
• Title: Enforcing Delayed-Impact Fairness Guarantees
• Authors: Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Bruno Castro da Silva
• Abstract summary: We introduce ELF, the first classification algorithm that provides high-confidence fairness guarantees in terms of long-term, or delayed, impact. We show experimentally that our algorithm can successfully mitigate long-term unfairness.
• Score: 21.368958668652652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e.g., applications involving education, employment, and lending), can inadvertently increase social inequality in the long term. This is because prior fairness-aware algorithms only consider static fairness constraints, such as equal opportunity or demographic parity. However, enforcing constraints of this type may result in models that have negative long-term impact on disadvantaged individuals and communities. We introduce ELF (Enforcing Long-term Fairness), the first classification algorithm that provides high-confidence fairness guarantees in terms of long-term, or delayed, impact. We prove that the probability that ELF returns an unfair solution is less than a user-specified tolerance and that (under mild assumptions), given sufficient training data, ELF is able to find and return a fair solution if one exists. We show experimentally that our algorithm can successfully mitigate long-term unfairness.

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