mlsanityData to 13 Oct 2026

ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

arXiv 1912.06166 ↗First posted 12 Dec 2019cs.CY stat.MLcited by 13

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Abstract (arXiv)

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.

Key facts. Cited by 13 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Dec 2019.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 0; 2024: 3; 2025: 2.

13 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2022The Fallacy of AI Functionalitycited by 88
  3. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  4. 2025Toward an Evaluation Science for Generative AI Systemscited by 43
  5. 2022The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligencecited by 26
  6. 2021"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?cited by 24
  7. 2024What's documented in AI? Systematic Analysis of 32K AI Model Cardscited by 10
  8. 2022Prescriptive and Descriptive Approaches to Machine-Learning Transparencycited by 7
  9. 2022Evaluating a Methodology for Increasing AI Transparency: A Case Studycited by 3
  10. 2024ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Studycited by 2
  11. 2021Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse eventscited by 0
  12. 2024Leveraging Ontologies to Document Bias in Datacited by 0
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  1. 2025Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagementcited by 0