mlsanityData to 7 Oct 2026

The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence

arXiv 2201.03954 ↗First posted 10 Jan 2022cs.LG cs.AIcited by 26

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Abstract (arXiv)

As the production of and reliance on datasets to produce automated decision-making systems (ADS) increases, so does the need for processes for evaluating and interrogating the underlying data. After launching the Dataset Nutrition Label in 2018, the Data Nutrition Project has made significant updates to the design and purpose of the Label, and is launching an updated Label in late 2020, which is previewed in this paper. The new Label includes context-specific Use Cases &Alerts presented through an updated design and user interface targeted towards the data scientist profile. This paper discusses the harm and bias from underlying training data that the Label is intended to mitigate, the current state of the work including new datasets being labeled, new and existing challenges, and further directions of the work, as well as Figures previewing the new label.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 10 Jan 2022.

Builds on

9 of 24 references.

References · 9

  1. 2018Datasheets for Datasetscited by 1,371
  2. 2018Model Cards for Model Reportingcited by 1,068
  3. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  4. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
  5. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
  6. 2018The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standardscited by 97
  7. 2019ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecyclescited by 13
  8. 2018A Nutritional Label for Rankingscited by 10
  9. 2019Talking datasets: Understanding data sensemaking behaviourscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 5; 2024: 9; 2025: 5; 2026: 1.

26 papers.

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  1. 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotationcited by 59
  2. 2022Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desideratacited by 28
  3. 2024Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Facecited by 27
  4. 2023Use case cards: a use case reporting framework inspired by the European AI Actcited by 14
  5. 2024What's documented in AI? Systematic Analysis of 32K AI Model Cardscited by 10
  6. 2024Benchmark Data Repositories for Better Benchmarkingcited by 10
  7. 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
  8. 2023Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessmentscited by 7
  9. 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
  10. 2023Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Educationcited by 5
  11. 2024Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They Representcited by 5
  12. 2024Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creatorscited by 5
14 more
  1. 2022Evaluating a Methodology for Increasing AI Transparency: A Case Studycited by 3
  2. 2024Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chaincited by 3
  3. 2024A Taxonomy of Challenges to Curating Fair Datasetscited by 2
  4. 2025TechOps: Technical Documentation Templates for the AI Actcited by 2
  5. 2022Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studiescited by 1
  6. 2022Documenting use cases in the affective computing domain using Unified Modeling Languagecited by 1
  7. 2023Datasheets for Machine Learning Sensorscited by 1
  8. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  9. 2025A Scoresheet for Explainable AIcited by 1
  10. 2026From Reflection to Repair: A Scoping Review of Dataset Documentation Toolscited by 1
  11. 2024Documentation Practices of Artificial Intelligencecited by 0
  12. 2025TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentationcited by 0
  13. 2025A case for data valuation transparency via DValCardscited by 0
  14. 2025Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspectivecited by 0