# Enforcing Delayed-Impact Fairness Guarantees

**Authors:** Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Bruno Castro da Silva  
**arXiv:** [2208.11744](https://arxiv.org/abs/2208.11744)  
**Date:** 2022-08-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.11744v1/

## Key points

- Authors address social inequality perpetuated by seemingly fair machine learning models in decision-making processes affecting areas such as education, employment, and lending
- Existing fairness-aware algorithms often focus on static fairness constraints like equal opportunity or demographic parity, which may lead to long-term negative impacts on disadvantaged individuals and communities
- Researchers introduce ELF (Enforcing Long-term Fairness), a novel classification algorithm designed to provide high-confidence fairness guarantees with regards to long-term or delayed impact
- Probability of ELF returning an unfair solution is less than a user-specified tolerance level
- ELF has the capability to identify and return a fair solution if one exists under mild assumptions and with sufficient training data
- Experimental validation showcases the effectiveness of ELF in mitigating long-term unfairness
- Introduction of a new approach that considers delayed impact in fairness considerations represents a significant advancement in ensuring equitable decision-making processes across various domains

## Summary

In their recent research paper titled "Enforcing Delayed-Impact Fairness Guarantees," authors Aline Weber, Blossom Metevier, Yuriy Brun, Philip S. Thomas, and Bruno Castro da Silva address the issue of social inequality perpetuated by seemingly fair machine learning models in decision-making processes affecting people's lives in areas such as education, employment, and lending. The authors highlight that existing fairness-aware algorithms often focus on static fairness constraints like equal opportunity or demographic parity, which may inadvertently lead to long-term negative impacts on disadvantaged individuals and communities. To tackle this challenge, the researchers introduce ELF (Enforcing Long-term Fairness), a novel classification algorithm designed to provide high-confidence fairness guarantees with regards to long-term or delayed impact. They demonstrate through rigorous analysis that the probability of ELF returning an unfair solution is less than a user-specified tolerance level. Additionally, under mild assumptions and with sufficient training data, ELF has the capability to identify and return a fair solution if one exists. Through experimental validation, the authors showcase the effectiveness of their algorithm in mitigating long-term unfairness. By introducing a new approach that considers delayed impact in fairness considerations, ELF represents a significant advancement in ensuring equitable decision-making processes across various domains. This research contributes valuable insights towards developing more socially responsible machine learning models that prioritize long-term fairness outcomes for marginalized populations.