mlsanityData to 7 Oct 2026

MIME: Minority Inclusion for Majority Group Enhancement of AI Performance

arXiv 2209.00746 ↗First posted 1 Sep 2022cs.LG cs.CVcited by 2

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Abstract (arXiv)

Several papers have rightly included minority groups in artificial intelligence (AI) training data to improve test inference for minority groups and/or society-at-large. A society-at-large consists of both minority and majority stakeholders. A common misconception is that minority inclusion does not increase performance for majority groups alone. In this paper, we make the surprising finding that including minority samples can improve test error for the majority group. In other words, minority group inclusion leads to majority group enhancements (MIME) in performance. A theoretical existence proof of the MIME effect is presented and found to be consistent with experimental results on six different datasets. Project webpage: https://visual.ee.ucla.edu/mime.htm/

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 59 references that are in the map. First posted on arXiv 1 Sep 2022.

Builds on

32 of 59 references.

References · 32

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20 more
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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023Synthetic Data in Healthcarecited by 8
  2. 2024Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culturecited by 6