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Evidence record 13060 · automatically gathered

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations. To evaluate unlearning effectiveness in multimodal large language models (MLLMs), prior works fine-tune models on fictitious identities, simulating unlearning requests on subsets of these IDs, which are typically uniformly distributed. However, in realistic scenarios, people from different demographic groups may request to be unlearned at different frequencies, potentiall

Record details

Published: 23 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation
Retrieved: 25 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (23 July 2026), “Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning,” evidence record 13060, https://ethics.ai/record/13060 (originally published by arXiv).

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