Post-processing for Individual Fairness

2021/10/26 by Felix Petersen, Debarghya Mukherjee, Petersen, Felix +5 · 13 citations
Computer Science · Mathematics · Social Sciences · #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Privacy-Preserving Technologies in Data #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.2110.13796

Published at NeurIPS 2021, Code @ https://github.com/Felix-Petersen/fairness-post-processing, Video @ https://www.youtube.com/watch?v=9PyKODDewPA

arxiv created 2021/10/26 · arxiv updated 2021/10/27

Abstract

Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it avoids expensive retraining. In this work, we propose general post-processing algorithms for individual fairness (IF). We consider a setting where the learner only has access to the predictions of the original model and a similarity graph between individuals, guiding the desired fairness constraints. We cast the IF post-processing problem as a graph smoothing problem corresponding to graph Laplacian regularization that preserves the desired "treat similar individuals similarly" interpretation. Our theoretical results demonstrate the connection of the new objective function to a local relaxation of the original individual fairness. Empirically, our post-processing algorithms correct individual biases in large-scale NLP models such as BERT, while preserving accuracy.

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• 'Rich Dad, Poor Lad': How do Large Language Models Contextualize Socioeconomic Factors in College Admission ?
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• SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
• Selecting for Less Discriminatory Algorithms: A Relational Search Framework for Navigating Fairness-Accuracy Trade-offs in Practice
• FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
• Towards Fair In-Context Learning with Tabular Foundation Models
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• FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair

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