# Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

arXiv: https://arxiv.org/abs/2103.03399 · HTML: https://mlsanity.com/paper/2103.03399/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 20 of 20)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems](https://mlsanity.com/paper/2011.12945/) (2020)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Bringing the People Back In: Contesting Benchmark Machine Learning Datasets](https://mlsanity.com/paper/2007.07399/) (2020)
- [Value-laden Disciplinary Shifts in Machine Learning](https://mlsanity.com/paper/1912.01172/) (2019)
- [Representativeness in Statistics, Politics, and Machine Learning](https://mlsanity.com/paper/2101.03827/) (2021)
- [Are All Training Examples Created Equal? An Empirical Study](https://mlsanity.com/paper/1811.12569/) (2018)
- [Narratives and Counternarratives on Data Sharing in Africa](https://mlsanity.com/paper/2103.01168/) (2021)

## Built on by (most cited first, 30 of 31)

- [Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images](https://mlsanity.com/paper/2405.20469/) (2024)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp](https://mlsanity.com/paper/2405.08209/) (2024)
- [The Data Addition Dilemma](https://mlsanity.com/paper/2408.04154/) (2024)
- [Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations](https://mlsanity.com/paper/2201.08186/) (2022)
- [Scaling Laws for the Value of Individual Data Points in Machine Learning](https://mlsanity.com/paper/2405.20456/) (2024)
- [RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models](https://mlsanity.com/paper/2401.09432/) (2023)
- [Mind the Graph When Balancing Data for Fairness or Robustness](https://mlsanity.com/paper/2406.17433/) (2024)
- [No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users](https://mlsanity.com/paper/2410.07589/) (2024)
- [Models of fairness in federated learning](https://mlsanity.com/paper/2112.00818/) (2021)
- [Delegating Data Collection in Decentralized Machine Learning](https://mlsanity.com/paper/2309.01837/) (2023)
- [Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations](https://mlsanity.com/paper/2305.02995/) (2023)
- [On Logical Extrapolation for Mazes with Recurrent and Implicit Networks](https://mlsanity.com/paper/2410.03020/) (2024)
- [Relative Scaling Laws for LLMs](https://mlsanity.com/paper/2510.24626/) (2025)
- [How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?](https://mlsanity.com/paper/2501.12535/) (2025)
- [Fairness in Forecasting of Observations of Linear Dynamical Systems](https://mlsanity.com/paper/2209.05274/) (2022)
- [Striving for data-model efficiency: Identifying data externalities on group performance](https://mlsanity.com/paper/2211.06348/) (2022)
- [Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry](https://mlsanity.com/paper/2409.03734/) (2024)
- [Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms](https://mlsanity.com/paper/2204.06664/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers](https://mlsanity.com/paper/2407.08689/) (2024)
- [Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation](https://mlsanity.com/paper/2501.14551/) (2025)
- [Mapping on a Budget: Optimizing Spatial Data Collection for ML](https://mlsanity.com/paper/2509.03749/) (2025)
- [Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication](https://mlsanity.com/paper/2501.06826/) (2025)
- [GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning](https://mlsanity.com/paper/2507.14725/) (2025)
- [Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance](https://mlsanity.com/paper/2604.21104/) (2026)
- [Predict Training Data Quality via Its Geometry in Metric Space](https://mlsanity.com/paper/2510.15970/) (2025)
- [Representation Invariance and Allocation: When Subgroup Balance Matters](https://mlsanity.com/paper/2512.09496/) (2025)
- [Measuring Dataset Diversity from a Geometric Perspective](https://mlsanity.com/paper/2602.09340/) (2026)
- [Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits](https://mlsanity.com/paper/2603.22339/) (2026)

## Abstract (arXiv)

Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a precise framework for understanding how dataset properties like diversity affect learning outcomes is largely lacking. By casting data collection as part of the learning process, we demonstrate that diverse representation in training data is key not only to increasing subgroup performances, but also to achieving population level objectives. Our analysis and experiments describe how dataset compositions influence performance and provide constructive results for using trends in existing data, alongside domain knowledge, to help guide intentional, objective-aware dataset design.