# From Reflection to Repair: A Scoping Review of Dataset Documentation Tools

arXiv: https://arxiv.org/abs/2602.15968 · HTML: https://mlsanity.com/paper/2602.15968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 138 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 30 of 57)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?](https://mlsanity.com/paper/2202.06675/) (2022)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://mlsanity.com/paper/2204.07931/) (2022)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus](https://mlsanity.com/paper/2105.02732/) (2021)
- [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation](https://mlsanity.com/paper/2206.08931/) (2022)
- [Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers](https://mlsanity.com/paper/2108.02922/) (2021)
- [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://mlsanity.com/paper/2105.05241/) (2021)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices](https://mlsanity.com/paper/2006.12358/) (2020)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice](https://mlsanity.com/paper/2210.03709/) (2022)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (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)
- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards](https://mlsanity.com/paper/2108.07374/) (2021)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora](https://mlsanity.com/paper/2608.09093/) (2026)

## Abstract (arXiv)

Dataset documentation is widely recognized as essential for the responsible development of automated systems. Despite growing efforts to support documentation through different kinds of artifacts, little is known about the motivations shaping documentation tool design or the factors hindering their adoption. We present a systematic review supported by mixed-methods analysis of 59 dataset documentation publications to examine the motivations behind building documentation tools, how authors conceptualize documentation practices, and how these tools connect to existing systems, regulations, and cultural norms. Our analysis shows four persistent patterns in dataset documentation conceptualization that potentially impede adoption and standardization: unclear operationalizations of documentation's value, decontextualized designs, unaddressed labor demands, and a tendency to treat integration as future work. Building on these findings, we propose a shift in Responsible AI tool design toward institutional rather than individual solutions, and outline actions the HCI community can take to enable sustainable documentation practices.