# Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning

arXiv: https://arxiv.org/abs/2508.16255 · HTML: https://mlsanity.com/paper/2508.16255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Sampling Permutations for Shapley Value Estimation](https://mlsanity.com/paper/2104.12199/) (2021)
- [Data Shapley in One Training Run](https://mlsanity.com/paper/2406.11011/) (2024)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [The Effects of Data Quality on Machine Learning Performance on Tabular Data](https://mlsanity.com/paper/2207.14529/) (2022)
- [2D-Shapley: A Framework for Fragmented Data Valuation](https://mlsanity.com/paper/2306.10473/) (2023)
- [On the Impact of Data Quality on Image Classification Fairness](https://mlsanity.com/paper/2305.01595/) (2023)
- [Shapley Value-based Contrastive Alignment for Multimodal Information Extraction](https://mlsanity.com/paper/2407.17854/) (2024)
- [Step-by-Step Data Cleaning Recommendations to Improve ML Prediction Accuracy](https://mlsanity.com/paper/2503.11366/) (2025)
- [Analytics Modelling over Multiple Datasets using Vector Embeddings](https://mlsanity.com/paper/2502.17060/) (2025)

## Abstract (arXiv)

As the volume and diversity of available datasets continue to increase, assessing data quality has become crucial for reliable and efficient Machine Learning analytics. A modern, game-theoretic approach for evaluating data quality is the notion of Data Shapley which quantifies the value of individual data points within a dataset. State-of-the-art methods to scale the NP-hard Shapley computation also face severe challenges when applied to large-scale datasets, limiting their practical use. In this work, we present a Data Shapley approach to identify a dataset's high-quality data tuples, Chunked Data Shapley (C-DaSh). C-DaSh scalably divides the dataset into manageable chunks and estimates the contribution of each chunk using optimized subset selection and single-iteration stochastic gradient descent. This approach drastically reduces computation time while preserving high quality results. We empirically benchmark our method on diverse real-world classification and regression tasks, demonstrating that C-DaSh outperforms existing Shapley approximations in both computational efficiency (achieving speedups between 80x - 2300x) and accuracy in detecting low-quality data regions. Our method enables practical measurement of dataset quality on large tabular datasets, supporting both classification and regression pipelines.