MIME: Minority Inclusion for Majority Group Enhancement of AI Performance
Abstract
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/.
Cite
Text
Chari et al. "MIME: Minority Inclusion for Majority Group Enhancement of AI Performance." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-19778-9_19Markdown
[Chari et al. "MIME: Minority Inclusion for Majority Group Enhancement of AI Performance." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/chari2022eccv-mime/) doi:10.1007/978-3-031-19778-9_19BibTeX
@inproceedings{chari2022eccv-mime,
title = {{MIME: Minority Inclusion for Majority Group Enhancement of AI Performance}},
author = {Chari, Pradyumna and Ba, Yunhao and Athreya, Shreeram and Kadambi, Achuta},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-19778-9_19},
url = {https://mlanthology.org/eccv/2022/chari2022eccv-mime/}
}