Risk-Aware Output Control Should be the Focus of Machine Unlearning
Abstract
Well-documented failure modes of machine learning (ML) systems, including bias, toxicity, hallucination, and leakage of sensitive information, has driven the rise of machine unlearning (MU). Most existing work, however, targets parameter-space indistinguishability, seeking unlearned models that are as close as possible to models retrained from scratch on retained data. In this paper, we argue that this objective is misaligned with practical and user-relevant concerns: MU should prioritize statistically guaranteed output-space control rather than parameter-space similarity. We contend that output control with statistical guarantees is the operationally meaningful notion of unlearning, as it supports rigorous and robust objectives, scales to modern deployment settings, and enables interpretable guarantees. We substantiate this position with empirical evidence and with recent theoretical results that, although not originally formulated in this framing, are consistent with it.
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BibTeX
@misc{alkhatib2026riskaware,
title = {{Risk-Aware Output Control Should be the Focus of Machine Unlearning}},
author = {Yahya Alkhatib and Yuyang Xiao and Wee Peng Tay},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35052},
eprint = {2610.35052},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35052},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Risk-Aware Output Control Should be the Focus of Machine Unlearning
AU - Yahya Alkhatib
AU - Yuyang Xiao
AU - Wee Peng Tay
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35052
UR - https://arxiv.org/abs/2610.35052
AB - Well-documented failure modes of machine learning (ML) systems, including bias, toxicity, hallucination, and leakage of sensitive information, has driven the rise of machine unlearning (MU). Most existing work, however, targets parameter-space indistinguishability, seeking unlearned models that are as close as possible to models retrained from scratch on retained data. In this paper, we argue that this objective is misaligned with practical and user-relevant concerns: MU should prioritize statistically guaranteed output-space control rather than parameter-space similarity. We contend that output control with statistical guarantees is the operationally meaningful notion of unlearning, as it supports rigorous and robust objectives, scales to modern deployment settings, and enables interpretable guarantees. We substantiate this position with empirical evidence and with recent theoretical results that, although not originally formulated in this framing, are consistent with it.
ER -