aiqbench analysis
This research redefines fairness in synthetic data generation by demonstrating that preserving the real-world distribution is sufficient to avoid disparate impact, challenging previous approaches that sought to correct observed biases. The authors identify specific failure modes in probabilistic graphical models caused by sampling and estimation errors, offering a group-wise modeling strategy as a practical baseline for mitigation.
Disparate Impact in Synthetic Data Generation
arXiv:2606.13105v2 Announce Type: replace Abstract: We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair SDG, that address the problem of correcting for undue biases in the observed distribution, hence redefining SDG as learning a distribution that is not that of the real data. By contrast, non-disparate impact is notably achieved when the synthetic and real distributions are the same. We expose reasons why SDG may fail to reach that solution and discuss why approximation and estimation errors occur and can be disparate across groups. We notably look into the expressive power of SDG methods relative to distribution complexity, sampling errors due to group proportions, and estimation errors induced by differential privacy mechanisms. We illustrate cases of disparate impact on both artificial and real-world data, focusing on SDG methods that rely on probabilistic graphical models. We also introduce a strategy of learning group-wise SDG models, that constitutes a simple baseline for analyzing and in some settings mitigating cases of disparate impact.
Abstract:We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair SDG, that address the problem of correcting for undue biases in the observed distribution, hence redefining SDG as learning a distribution that is not that of the real data. By contrast, non-disparate impact is notably achieved when the synthetic and real distributions are the same. We expose reasons why SDG may fail to reach that solution and discuss why approximation and estimation errors occur and can be disparate across groups. We notably look into the expressive power of SDG methods relative to distribution complexity, sampling errors due to group proportions, and estimation errors induced by differential privacy mechanisms. We illustrate cases of disparate impact on both artificial and real-world data, focusing on SDG methods that rely on probabilistic graphical models. We also introduce a strategy of learning group-wise SDG models, that constitutes a simple baseline for analyzing and in some settings mitigating cases of disparate impact.
Accepted at ECML PKDD 2026 (Research Track). First published in Lecture Notes in Computer Science (LNAI), volume 16946, pp 95-113, in 2026 by Springer Nature. Version of Record available online at this https URL. This expanded version includes appendices that detail experimental results, and a minor rewrite of definition 2
Machine Learning (cs.LG)
arXiv:2606.13105 [cs.LG]
(or arXiv:2606.13105v2 [cs.LG] for this version)
https://doi.org/10.1007/978-3-032-37673-2_6 DOI(s) linking to related resources
Full text under the publisher's open licence · 248 words
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