# Adaptive Sampling Strategies to Construct Equitable Training Datasets

arXiv: https://arxiv.org/abs/2202.01327 · HTML: https://mlsanity.com/paper/2202.01327/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 27 of 27)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://mlsanity.com/paper/2111.09344/) (2021)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [A Confidence-Based Approach for Balancing Fairness and Accuracy](https://mlsanity.com/paper/1601.05764/) (2016)
- [From Parity to Preference-based Notions of Fairness in Classification](https://mlsanity.com/paper/1707.00010/) (2017)
- [Counterfactual Risk Assessments, Evaluation, and Fairness](https://mlsanity.com/paper/1909.00066/) (2019)
- [Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds](https://mlsanity.com/paper/2009.02841/) (2020)
- [Equal Opportunity in Online Classification with Partial Feedback](https://mlsanity.com/paper/1902.02242/) (2019)
- [Active Sampling for Min-Max Fairness](https://mlsanity.com/paper/2006.06879/) (2020)
- [FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity](https://mlsanity.com/paper/1808.07261/) (2018)
- [Fair Active Learning](https://mlsanity.com/paper/2001.01796/) (2020)
- [Active Fairness in Algorithmic Decision Making](https://mlsanity.com/paper/1810.00031/) (2018)
- [ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery](https://mlsanity.com/paper/2011.05479/) (2020)
- [AViD Dataset: Anonymized Videos from Diverse Countries](https://mlsanity.com/paper/2007.05515/) (2020)
- [Fair Allocation through Selective Information Acquisition](https://mlsanity.com/paper/1911.02715/) (2019)
- [Can Active Learning Preemptively Mitigate Fairness Issues?](https://mlsanity.com/paper/2104.06879/) (2021)

## Built on by (most cited first, 18 of 18)

- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [Beyond the Safeguards: Exploring the Security Risks of ChatGPT](https://mlsanity.com/paper/2305.08005/) (2023)
- [Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans](https://mlsanity.com/paper/2209.13020/) (2022)
- [A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models](https://mlsanity.com/paper/2311.11415/) (2023)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- ["You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning](https://mlsanity.com/paper/2206.12183/) (2022)
- [More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias](https://mlsanity.com/paper/2207.07723/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Designing Equitable Algorithms](https://mlsanity.com/paper/2302.09157/) (2023)
- [Fair Active Learning in Low-Data Regimes](https://mlsanity.com/paper/2312.08559/) (2023)
- [Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach](https://mlsanity.com/paper/2402.11338/) (2024)
- [Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks](https://mlsanity.com/paper/2408.12806/) (2024)
- [Mapping on a Budget: Optimizing Spatial Data Collection for ML](https://mlsanity.com/paper/2509.03749/) (2025)
- [Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?](https://mlsanity.com/paper/2211.08735/) (2022)
- [An Operational Perspective to Fairness Interventions: Where and How to Intervene](https://mlsanity.com/paper/2302.01574/) (2023)
- [Bias-Aware Mislabeling Detection via Decoupled Confident Learning](https://mlsanity.com/paper/2507.07216/) (2025)
- [FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data](https://mlsanity.com/paper/2606.23515/) (2026)

## Abstract (arXiv)

In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups. One factor contributing to these performance gaps is a lack of representation in the data the models are trained on. It is often unclear, however, how to operationalize representativeness in specific applications. Here we formalize the problem of creating equitable training datasets, and propose a statistical framework for addressing this problem. We consider a setting where a model builder must decide how to allocate a fixed data collection budget to gather training data from different subgroups. We then frame dataset creation as a constrained optimization problem, in which one maximizes a function of group-specific performance metrics based on (estimated) group-specific learning rates and costs per sample. This flexible approach incorporates preferences of model-builders and other stakeholders, as well as the statistical properties of the learning task. When data collection decisions are made sequentially, we show that under certain conditions this optimization problem can be efficiently solved even without prior knowledge of the learning rates. To illustrate our approach, we conduct a simulation study of polygenic risk scores on synthetic genomic data -- an application domain that often suffers from non-representative data collection. We find that our adaptive sampling strategy outperforms several common data collection heuristics, including equal and proportional sampling, demonstrating the value of strategic dataset design for building equitable models.