论文标题
MIME：多数族裔组的少数派加入AI表现
MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
论文作者
论文摘要
有几篇论文正确包括人工智能（AI）培训数据中的少数群体，以改善对少数群体和/或一般社会的测试推论。一般社会由少数利益相关者和多数利益相关者组成。一个普遍的误解是，少数群体的包容性不会单独提高多数群体的绩效。在本文中，我们令人惊讶的是，包括少数样本可以改善多数族裔的测试错误。换句话说，少数群体的包容性会导致多数群体增强（MIME）的性能。提出了哑剧效应的理论存在证明，并发现与六个不同数据集的实验结果一致。项目网页：https：//visual.ee.ucla.edu/mime.htm/
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/
