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

arXiv: https://arxiv.org/abs/2201.03954 · HTML: https://mlsanity.com/paper/2201.03954/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches](https://mlsanity.com/paper/1912.05100/) (2019)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles](https://mlsanity.com/paper/1912.06166/) (2019)
- [A Nutritional Label for Rankings](https://mlsanity.com/paper/1804.07890/) (2018)
- [Talking datasets: Understanding data sensemaking behaviours](https://mlsanity.com/paper/1911.09041/) (2019)

## Built on by (most cited first, 26 of 26)

- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [What's documented in AI? Systematic Analysis of 32K AI Model Cards](https://mlsanity.com/paper/2402.05160/) (2024)
- [Benchmark Data Repositories for Better Benchmarking](https://mlsanity.com/paper/2410.24100/) (2024)
- [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022)
- [Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments](https://mlsanity.com/paper/2312.06153/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Racial/Ethnic Categories in AI and Algorithmic Fairness: Why They Matter and What They Represent](https://mlsanity.com/paper/2404.06717/) (2024)
- [Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators](https://mlsanity.com/paper/2409.00252/) (2024)
- [Evaluating a Methodology for Increasing AI Transparency: A Case Study](https://mlsanity.com/paper/2201.13224/) (2022)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [A Taxonomy of Challenges to Curating Fair Datasets](https://mlsanity.com/paper/2406.06407/) (2024)
- [Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies](https://mlsanity.com/paper/2205.01071/) (2022)
- [Documenting use cases in the affective computing domain using Unified Modeling Language](https://mlsanity.com/paper/2209.09666/) (2022)
- [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023)
- [Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI](https://mlsanity.com/paper/2404.08221/) (2024)
- [A Scoresheet for Explainable AI](https://mlsanity.com/paper/2502.09861/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Documentation Practices of Artificial Intelligence](https://mlsanity.com/paper/2406.18620/) (2024)
- [TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentation](https://mlsanity.com/paper/2505.17841/) (2025)
- [A case for data valuation transparency via DValCards](https://mlsanity.com/paper/2506.23349/) (2025)
- [Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective](https://mlsanity.com/paper/2509.10432/) (2025)

## 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.