Disparate Impact in Synthetic Data Generation

arXiv:2606.13105 · cs.LG · Submitted 2026-06-11 · Read on arXiv

cs.LG

Submitted: 2026-06-11

Updated: 2026-09-14

Comments: 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 https://doi.org/10.1007/978-3-032-37673-2_6. This expanded version includes appendices that detail experimental results, and a minor rewrite of definition 2

DOI: 10.1007/978-3-032-37673-2_6

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Synthetic Data Generation — 26× in this paper · explained in 5 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• AUROC — 9× in this paper · explained in 2 episodes
• Differential Privacy — 7× in this paper · explained in 4 episodes
• Differentially Private — 7× in this paper · explained in 3 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• American Community Survey — 2× in this paper
• Benchmarking — 2× in this paper · explained in 4 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Algorithmic Bias — 1× in this paper · explained in 1 episode
• Differential Privacy (DP) — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

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