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

arXiv: https://arxiv.org/abs/1912.06166 · HTML: https://mlsanity.com/paper/1912.06166/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 13 of 13)

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- [Prescriptive and Descriptive Approaches to Machine-Learning Transparency](https://mlsanity.com/paper/2204.13582/) (2022)
- [Evaluating a Methodology for Increasing AI Transparency: A Case Study](https://mlsanity.com/paper/2201.13224/) (2022)
- [ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study](https://mlsanity.com/paper/2401.06513/) (2024)
- [Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events](https://mlsanity.com/paper/2201.00692/) (2021)
- [Leveraging Ontologies to Document Bias in Data](https://mlsanity.com/paper/2407.00509/) (2024)
- [Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement](https://mlsanity.com/paper/2509.02624/) (2025)

## 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.