aiqbench analysis
The study reveals how the multiplicity of near-optimal models in real-world ML pipelines creates a complex trade-off between reactive robustness against attacks and increased information leakage. While switching between diverse models offers a defense mechanism without retraining, expanding this set exposes more training data patterns to potential adversaries. This research provides critical theoretical and empirical insights into managing the dual role of model ensembles as both security assets and privacy risks in trustworthy machine learning systems.
The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning
arXiv:2511.21799v2 Announce Type: replace Abstract: Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show that this multiplicity reshapes key aspects of trustworthiness. At the individual-model level, sparse interpretable models tend to preserve privacy but are fragile to adversarial attacks. In contrast, the diversity within a large Rashomon set enables reactive robustness: even when an attack compromises one model, a practitioner can switch to a different near-optimal model that remains accurate, without retraining. However, the same diversity increases information leakage, as disclosing more near-optimal models provides an attacker with progressively richer views of the training data. This produces a robustness-privacy trade-off governed by diversity, which we analyze theoretically and empirically. Beyond this trade-off, Rashomon sets are stable under small distribution shifts, so a set computed once remains valid under such shifts without re-computation. Our results highlight the dual role of Rashomon sets as both a resource and a risk for trustworthy ML.
Abstract:Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show that this multiplicity reshapes key aspects of trustworthiness. At the individual-model level, sparse interpretable models tend to preserve privacy but are fragile to adversarial attacks. In contrast, the diversity within a large Rashomon set enables reactive robustness: even when an attack compromises one model, a practitioner can switch to a different near-optimal model that remains accurate, without retraining. However, the same diversity increases information leakage, as disclosing more near-optimal models provides an attacker with progressively richer views of the training data. This produces a robustness-privacy trade-off governed by diversity, which we analyze theoretically and empirically. Beyond this trade-off, Rashomon sets are stable under small distribution shifts, so a set computed once remains valid under such shifts without re-computation. Our results highlight the dual role of Rashomon sets as both a resource and a risk for trustworthy ML.
Machine Learning (cs.LG)
arXiv:2511.21799 [cs.LG]
(or arXiv:2511.21799v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2511.21799 Focus to learn more arXiv-issued DOI via DataCite
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