# Machine Learning practices and infrastructures

arXiv: https://arxiv.org/abs/2307.06518 · HTML: https://mlsanity.com/paper/2307.06518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 153 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices](https://mlsanity.com/paper/2006.12358/) (2020)
- [Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches](https://mlsanity.com/paper/2205.04460/) (2022)
- [Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination](https://mlsanity.com/paper/1912.06171/) (2019)
- [Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development](https://mlsanity.com/paper/2110.02932/) (2021)
- [Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud](https://mlsanity.com/paper/2109.03285/) (2021)
- [Defining AI in Policy versus Practice](https://mlsanity.com/paper/1912.11095/) (2019)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Reflexive Design for Fairness and Other Human Values in Formal Models](https://mlsanity.com/paper/2010.05084/) (2020)
- [German AI Start-Ups and AI Ethics: Using A Social Practice Lens for Assessing and Implementing Socio-Technical Innovation](https://mlsanity.com/paper/2206.09978/) (2022)
- [Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study](https://mlsanity.com/paper/1804.05492/) (2018)
- [Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects](https://mlsanity.com/paper/1802.02938/) (2018)
- [The Objective Function: Science and Society in the Age of Machine Intelligence](https://mlsanity.com/paper/2209.10418/) (2022)
- [Code Duplication and Reuse in Jupyter Notebooks](https://mlsanity.com/paper/2005.13709/) (2020)
- [Stack Overflow: A Code Laundering Platform?](https://mlsanity.com/paper/1703.03897/) (2017)
- [Predicting Good Configurations for GitHub and Stack Overflow Topic Models](https://mlsanity.com/paper/1804.04749/) (2018)
- [Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger](https://mlsanity.com/paper/2010.07244/) (2020)
- [Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond](https://mlsanity.com/paper/1803.11045/) (2018)

## Built on by (most cited first, 2 of 2)

- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [Advancing Quantum Information Science Pre-College Education: The Case for Learning Sciences Collaboration](https://mlsanity.com/paper/2508.00668/) (2025)

## Abstract (arXiv)

Machine Learning (ML) systems, particularly when deployed in high-stakes domains, are deeply consequential. They can exacerbate existing inequities, create new modes of discrimination, and reify outdated social constructs. Accordingly, the social context (i.e. organisations, teams, cultures) in which ML systems are developed is a site of active research for the field of AI ethics, and intervention for policymakers. This paper focuses on one aspect of social context that is often overlooked: interactions between practitioners and the tools they rely on, and the role these interactions play in shaping ML practices and the development of ML systems. In particular, through an empirical study of questions asked on the Stack Exchange forums, the use of interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML practices is explored. I find that interactive computing platforms are used in a host of learning and coordination practices, which constitutes an infrastructural relationship between interactive computing platforms and ML practitioners. I describe how ML practices are co-evolving alongside the development of interactive computing platforms, and highlight how this risks making invisible aspects of the ML life cycle that AI ethics researchers' have demonstrated to be particularly salient for the societal impact of deployed ML systems.