Inference-Time Rule Eraser: Fair Recognition via Distilling and Removing Biased Rules

• URL: http://arxiv.org/abs/2404.04814v4
• Date: Tue, 27 Aug 2024 04:43:10 GMT
• Title: Inference-Time Rule Eraser: Fair Recognition via Distilling and Removing Biased Rules
• Authors: Yi Zhang, Dongyuan Lu, Jitao Sang,
• Abstract summary: Machine learning models often make predictions based on biased features such as gender, race, and other social attributes. Traditional approaches to addressing this issue involve retraining or fine-tuning neural networks with fairness-aware optimization objectives. We introduce the Inference-Time Rule Eraser (Eraser), a novel method designed to address fairness concerns.
• Score: 16.85221824455542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models often make predictions based on biased features such as gender, race, and other social attributes, posing significant fairness risks, especially in societal applications, such as hiring, banking, and criminal justice. Traditional approaches to addressing this issue involve retraining or fine-tuning neural networks with fairness-aware optimization objectives. However, these methods can be impractical due to significant computational resources, complex industrial tests, and the associated CO2 footprint. Additionally, regular users often fail to fine-tune models because they lack access to model parameters In this paper, we introduce the Inference-Time Rule Eraser (Eraser), a novel method designed to address fairness concerns by removing biased decision-making rules from deployed models during inference without altering model weights. We begin by establishing a theoretical foundation for modifying model outputs to eliminate biased rules through Bayesian analysis. Next, we present a specific implementation of Eraser that involves two stages: (1) distilling the biased rules from the deployed model into an additional patch model, and (2) removing these biased rules from the output of the deployed model during inference. Extensive experiments validate the effectiveness of our approach, showcasing its superior performance in addressing fairness concerns in AI systems.

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