# Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups

arXiv: https://arxiv.org/abs/2411.04761 · HTML: https://mlsanity.com/paper/2411.04761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence](https://mlsanity.com/paper/2204.02570/) (2022)
- [On Obtaining Stable Rankings](https://mlsanity.com/paper/1804.10990/) (2018)
- [Big Holes in Big Data: A Monte Carlo Algorithm for Detecting Large Hyper-rectangles in High Dimensional Data](https://mlsanity.com/paper/1704.00683/) (2017)
- [RRR: Rank-Regret Representative](https://mlsanity.com/paper/1802.10303/) (2018)
- [Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach](https://mlsanity.com/paper/2306.13868/) (2023)
- [Reliability Evaluation of Individual Predictions: A Data-centric Approach](https://mlsanity.com/paper/2204.07682/) (2022)

## Built on by (most cited first, 1 of 1)

- [Unbiased Binning for Fairness-aware Attribute Representation](https://mlsanity.com/paper/2509.21785/) (2025)

## Abstract (arXiv)

Due to a variety of reasons, such as privacy, data in the wild often misses the grouping information required for identifying minorities. On the other hand, it is known that machine learning models are only as good as the data they are trained on and, hence, may underperform for the under-represented minority groups. The missing grouping information presents a dilemma for responsible data scientists who find themselves in an unknown-unknown situation, where not only do they not have access to the grouping attributes but do not also know what groups to consider. This paper is an attempt to address this dilemma. Specifically, we propose a minority mining problem, where we find vectors in the attribute space that reveal potential groups that are under-represented and under-performing. Technically speaking, we propose a geometric transformation of data into a dual space and use notions such as the arrangement of hyperplanes to design an efficient algorithm for the problem in lower dimensions. Generalizing our solution to the higher dimensions is cursed by dimensionality. Therefore, we propose a solution based on smart exploration of the search space for such cases. We conduct comprehensive experiments using real-world and synthetic datasets alongside the theoretical analysis. Our experiment results demonstrate the effectiveness of our proposed solutions in mining the unknown, under-represented, and under-performing minorities.