The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track

2024-10-29

arXiv.org

AI Summary

Machine learning (ML) suffers from weak data curation practices and insufficient documentation of ethical, environmental, and data management information. Method: We systematically evaluated 60 datasets from the NeurIPS Datasets and Benchmarks Track (2021–2023), introducing bibliometric data cataloging theory from library and information science to ML for the first time. We developed a literature-driven, four-dimensional evaluation framework—assessing documentation completeness, ethical impact, environmental footprint, and data management—and designed an actionable, structured rubric alongside an open-source assessment toolkit. Contribution/Results: We released the first exemplar metadata repository showcasing best practices. Our analysis revealed widespread deficiencies across all four dimensions. Based on these findings, we formulated actionable guidelines for conference reviewers and community adoption. All artifacts—including framework, rubric, toolkit, and metadata—are openly shared to advance ML datasets toward higher quality, reusability, and standardization.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a literature review of data curation principles. We use the framework to assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research.

Problem

• Data Curation
• Machine Learning
• Ethical Considerations

Innovation

• Data Curation Framework
• NeurIPS Dataset Quality Assessment
• Machine Learning Data Practices