[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120717-en":3,"doc-seo-120717-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120717,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning practices and infrastructures - Research focus","Machine Learning (ML) systems deployed in high-stakes settings can intensify existing inequities and generate new discrimination, shaped by the social context in which they are built. This paper examines a frequently overlooked dimension of that context: how practitioners interact with the tools they use to research, develop, and deploy ML systems. Using empirical study of questions from Stack Exchange and analysis of interactive computing platforms, it shows how these platforms support learning, coordination, and co-evolution with ML practice.","Machine Learning practices and infrastructures  \nGlen Berman  \n[glen.berman@anu.edu.au](glen.berman@anu.edu.au)  \nAustralian National University  \nCanberra, ACT, Australia  \narXiv :2307 .06518v1 [ cs .CY] 13 Jul 2023  \nABSTRACT  \nMachine Learning (ML) systems, particularly when deployed in high-stakes domains, are deeply consequential. They can exacerbate existing inequities, create new modes of discrimination, andreify outdated social constructs.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.  \nCCS CONCEPTS  \n• Computing methodologies → Machine learning; • Social and professional topics → Socio-technical systems; History of software.  \nKEYWORDS  \nmachine learning, infrastructure studies, social practice  \nACM Reference Format:  \nGlen Berman. 2023. Machine Learning practices and infrastructures. In Proceedings of the AAI/ACM Conference on Artificial Intelligence, Ethics, and Society, August 08–10, 2023, Montreal, QC. ACM, New York, NY, USA, 16 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 INTRODUCTION  \nIt follows from the notion that Machine Learning (ML) systemsought to be thought of as sociotechnical systems [122]—i.e. systems that are socially constructed, requiring both human actors and machines to work [36]—that the social context in which an  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions [from permissions@acm.org](from permissions@acm.org).  \nAIES ’23, August, 2023, Montreal, QC, Canada © 2023 Association for Computing Machinery.  \nACM ISBN 978-1-4503-XXXX-X/18/06. . . $15.00 [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \nML systems is researched, developed, and deployed is likely to shape the characteristics of that system. Given the increasing rate of ML system deployment in high-stakes domains, and widespread evidence of ML systems failing to meet societal expectations [e.g. 25, 69, 94, 123], a key question for ML field relates to infrastructuralisation and its implications for ML practices and deployed ML systems. This paper begins to address this question, by attending to one aspect of social context—interactions between ML practitioners and the tools they use to research, build, and deploy ML systems—and demonstrating the relevance of this context to concerns raised by AI ethics researchers.  \nThe social context of ML system development has been studied in AI ethics [e.g. 15, 33, 5","cbCaicv5onJ6snoC","https://ap.wps.com/l/cbCaicv5onJ6snoC","pdf",1393398,1,16,"English","en",105,"# Introduction\n## Sociotechnical framing and problem motivation\n## Research question and approach\n## Method: topic modeling and qualitative analysis\n# Findings: platform use in ML practices\n## Learning practices\n## Coordination across infrastructures\n## Data curation and processing\n## Model training and evaluation\n# Implications for AI ethics and visibility of the ML life cycle","[{\"question\":\"What aspect of ML social context does the paper focus on?\",\"answer\":\"It focuses on interactions between ML practitioners and the tools used to research, build, and deploy ML systems, especially how these interactions shape ML practices.\"},{\"question\":\"How does the paper study the use of interactive computing platforms?\",\"answer\":\"It analyzes practitioner questions from Stack Exchange using a probabilistic topic model and complements this with qualitative text analysis of a smaller sample.\"},{\"question\":\"In which stages of ML system development are interactive computing platforms commonly used?\",\"answer\":\"They are used across learning and coordination practices, with emphasis on data curation and processing as well as model training and evaluation.\"}]","Machine Learning practices and infrastructures - 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