# Assessing and Remedying Coverage for a Given Dataset

arXiv: https://arxiv.org/abs/1810.06742 · HTML: https://mlsanity.com/paper/1810.06742/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 21 of 21)

- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Interpretable Data-Based Explanations for Fairness Debugging](https://mlsanity.com/paper/2112.09745/) (2021)
- [OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning](https://mlsanity.com/paper/2103.09055/) (2021)
- [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching](https://mlsanity.com/paper/2307.02726/) (2023)
- [Detecting discriminatory risk through data annotation based on Bayesian inferences](https://mlsanity.com/paper/2101.11358/) (2021)
- [Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities](https://mlsanity.com/paper/2402.01071/) (2024)
- [Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms](https://mlsanity.com/paper/2204.06664/) (2022)
- [Designing Data: Proactive Data Collection and Iteration for Machine Learning](https://mlsanity.com/paper/2301.10319/) (2023)
- [Non-Invasive Fairness in Learning through the Lens of Data Drift](https://mlsanity.com/paper/2303.17566/) (2023)
- [Towards Assessing Data Bias in Clinical Trials](https://mlsanity.com/paper/2212.09633/) (2022)
- [Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education](https://mlsanity.com/paper/2109.07908/) (2021)
- [Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets](https://mlsanity.com/paper/2209.06316/) (2022)
- [Detection of Groups with Biased Representation in Ranking](https://mlsanity.com/paper/2301.00719/) (2022)
- [Dataset Representativeness and Downstream Task Fairness](https://mlsanity.com/paper/2407.00170/) (2024)
- [Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets](https://mlsanity.com/paper/2408.01375/) (2024)
- [Data Acquisition for Improving Model Fairness using Reinforcement Learning](https://mlsanity.com/paper/2412.03009/) (2024)
- [SourceSplice: Source Selection for Machine Learning Tasks](https://mlsanity.com/paper/2507.22186/) (2025)
- [Data Bias Mitigation under Coverage Constraints & The Price of Fairness](https://mlsanity.com/paper/2606.20461/) (2026)

## Abstract (arXiv)

Data analysis impacts virtually every aspect of our society today. Often, this analysis is performed on an existing dataset, possibly collected through a process that the data scientists had limited control over. The existing data analyzed may not include the complete universe, but it is expected to cover the diversity of items in the universe. Lack of adequate coverage in the dataset can result in undesirable outcomes such as biased decisions and algorithmic racism, as well as creating vulnerabilities such as opening up room for adversarial attacks. In this paper, we assess the coverage of a given dataset over multiple categorical attributes. We first provide efficient techniques for traversing the combinatorial explosion of value combinations to identify any regions of attribute space not adequately covered by the data. Then, we determine the least amount of additional data that must be obtained to resolve this lack of adequate coverage. We confirm the value of our proposal through both theoretical analyses and comprehensive experiments on real data.