Training individually fair ML models with Sensitive Subspace Robustness

Mikhail Yurochkin · Amanda Bower · Yuekai Sun

57 citations0/0 files storedarXiv (Cornell University)

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Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Explainable Artificial Intelligence (XAI)

Abstract

We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.

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References

2004Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination 2012Fairness through awareness 2014Big Data's Disparate Impact 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2010Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2018Mitigating Unwanted Biases with Adversarial Learning 2012Robust Solutions of Optimization Problems Affected by Uncertain Probabilities

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2024Assessing trustworthy AI: Technical and legal perspectives of fairness in AI 2020Two Simple Ways to Learn Individual Fairness Metrics from Data 2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization 2021A Statistical Test for Probabilistic Fairness 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects

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