Fair Representation Learning with Controllable High Confidence Guarantees via Adversarial Inference

2025-10-23

AI Summary

This work addresses the lack of verifiable probabilistic guarantees for group fairness in downstream predictions within representation learning. We propose FRG, the first representation learning framework enabling high-confidence (PAC-style) fairness control. FRG jointly optimizes representations and fairness constraints via adversarial inference, ensuring—uniformly across arbitrary downstream models—that demographic disparities (e.g., mean difference, prediction error difference) are bounded within a user-specified threshold $varepsilon$ with probability at least $1-delta$. The framework integrates high-confidence risk bounding with an adjustable fairness boundary mechanism, offering theoretical guarantees and implementation simplicity. Experiments on three real-world datasets demonstrate that FRG consistently achieves superior and more robust unfairness mitigation compared to six state-of-the-art methods, across diverse downstream models—including logistic regression, MLPs, and XGBoost—and multiple prediction tasks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Representation Learning for Vision
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Representation learning is increasingly applied to generate representations that generalize well across multiple downstream tasks. Ensuring fairness guarantees in representation learning is crucial to prevent unfairness toward specific demographic groups in downstream tasks. In this work, we formally introduce the task of learning representations that achieve high-confidence fairness. We aim to guarantee that demographic disparity in every downstream prediction remains bounded by a *user-defined* error threshold $ε$, with *controllable* high probability. To this end, we propose the ***F**air **R**epresentation learning with high-confidence **G**uarantees (FRG)* framework, which provides these high-confidence fairness guarantees by leveraging an optimized adversarial model. We empirically evaluate FRG on three real-world datasets, comparing its performance to six state-of-the-art fair representation learning methods. Our results demonstrate that FRG consistently bounds unfairness across a range of downstream models and tasks.

Problem

• Learning fair representations with user-defined high-confidence guarantees
• Bounding demographic disparity in downstream predictions via adversarial inference
• Ensuring controllable fairness across multiple tasks and models

Innovation

• Adversarial inference optimizes fair representation learning
• User-defined error threshold controls demographic disparity bounds
• High-confidence guarantees ensure fairness across downstream tasks