Research Paper#Algorithmic Fairness, AI Ethics, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:23
Statistical Guarantees for Less Discriminatory Algorithm Search
Analysis
This paper addresses the crucial problem of algorithmic discrimination in high-stakes domains. It proposes a practical method for firms to demonstrate a good-faith effort in finding less discriminatory algorithms (LDAs). The core contribution is an adaptive stopping algorithm that provides statistical guarantees on the sufficiency of the search, allowing developers to certify their efforts. This is particularly important given the increasing scrutiny of AI systems and the need for accountability.
Key Takeaways
- •Addresses the problem of algorithmic discrimination in critical areas like employment and housing.
- •Proposes a method for firms to demonstrate a good-faith effort in finding less discriminatory algorithms.
- •Introduces an adaptive stopping algorithm with statistical guarantees to certify the sufficiency of the search.
- •Provides a framework for incorporating stronger assumptions to obtain stronger bounds.
- •Validates the method on real-world datasets.
Reference
“The paper formalizes LDA search as an optimal stopping problem and provides an adaptive stopping algorithm that yields a high-probability upper bound on the gains achievable from a continued search.”