Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data

• URL: http://arxiv.org/abs/2310.03146v3
• Date: Fri, 13 Sep 2024 16:55:40 GMT
• Title: Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
• Authors: Son Nguyen, Adam Wang, Albert Montillo,
• Abstract summary: We introduce the Fair Mixed Effects Deep Learning (Fair MEDL) framework. Fair MEDL quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) We incorporate adversarial debiasing to promote fairness across three key metrics: Equalized Odds, Demographic Parity, and Counterfactual Fairness.
• Score: 6.596656267996196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional deep learning (DL) models face two key challenges. First, they assume training samples are independent and identically distributed, an assumption often violated in real-world datasets where samples are grouped by shared measurements (e.g., participants or cells). This leads to performance degradation, limited generalization, and confounding issues, causing Type 1 and Type 2 errors. Second, DL models typically prioritize overall accuracy, often overlooking fairness across underrepresented groups, leading to biased outcomes in critical areas such as loan approvals and healthcare decisions. To address these issues, we introduce the Fair Mixed Effects Deep Learning (Fair MEDL) framework. Fair MEDL quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) through 1) a cluster adversary for learning invariant FE, 2) a Bayesian neural network for RE, and 3) a mixing function combining FE and RE for final predictions. Additionally, we incorporate adversarial debiasing to promote fairness across three key metrics: Equalized Odds, Demographic Parity, and Counterfactual Fairness. Our method also identifies and de-weights confounding probes, improving interpretability. Evaluated on three datasets from finance and healthcare, Fair MEDL improves fairness by up to 73% for age, 47% for race, 83% for sex, and 26% for marital status, while maintaining robust predictive performance. Our implementation is publicly available on GitHub.

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