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In human networks, nodes belonging to a marginalized group often have a disproportionate rate of unknown or missing features. This, in conjunction with graph structure and known feature biases, can cause graph feature imputation algorithms to predict values for unknown features that make the marginalized group's feature values more distinct from the the dominant group's feature values than they are in reality. We call this distinction the discrimination risk. We prove that a higher discrimination risk can amplify the unfairness of a machine learning model applied to the imputed data. We then formalize a general graph feature imputation framework called mean aggregation imputation and theoretically and empirically characterize graphs in which applying this framework can yield feature values with a high discrimination risk. We propose a simple algorithm to ensure mean aggregation-imputed features provably have a low discrimination risk, while minimally sacrificing reconstruction error (with respect to the imputation objective). We evaluate the fairness and accuracy of our solution on synthetic and real-world credit networks.
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This content will become publicly available on March 5, 2026
Information-theoretic quantification of inherent discrimination bias in training data for supervised learning
Algorithmic fairness research has mainly focused on adapting learning models to mitigate discrimination based on protected attributes, yet understanding inherent biases in training data remains largely unexplored. Quantifying these biases is crucial for informed data engineering, as data mining and model development often occur separately. We address this by developing an information-theoretic framework to quantify the marginal impacts of dataset features on the discrimination bias of downstream predictors. We postulate a set of desired properties for candidate discrimination measures and derive measures that (partially) satisfy them. Distinct sets of these properties align with distinct fairness criteria like demographic parity or equalized odds, which we show can be in disagreement and not simultaneously satisfied by a single measure. We use the Shapley value to determine individual features’ contributions to overall discrimination, and prove its effectiveness in eliminating redundancy. We validate our measures through a comprehensive empirical study on numerous real-world and synthetic datasets. For synthetic data, we use a parametric linear structural causal model to generate diverse data correlation structures. Our analysis provides empirically validated guidelines for selecting discrimination measures based on data conditions and fairness criteria, establishing a robust framework for quantifying inherent discrimination bias in data
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- PAR ID:
- 10640734
- Publisher / Repository:
- Openreview.net: https://openreview.net/pdf?id=ZsqigefFO8 This work has been accepted for presentation at the "2nd Workshop on Navigating and Addressing Data Problems for Foundation Models (DATA-FM)" at ICLR 2025.
- Date Published:
- Subject(s) / Keyword(s):
- Algorithmic fairness Model agnostic bias quantification Fair data engineering Information theoretic measures Partial information decomposition Shapley value aggregation.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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