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International Conference on Information

iConference 2020: Sustainable Digital Communities pp 651–664Cite as

AI Models and Their Worlds: Investigating Data-Driven, AI/ML Ecosystems Through a Work Practices Lens

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12051))

Abstract

When we invoke the “future of work,” to whose work do we refer? This paper considers everyday work practices through which contemporary artificial intelligence (AI) and machine learning (ML) ecosystems are made possible. The “future of work” is often talked about in relation to the anticipated domain settings where AI/ML systems might be implemented and the labor conditions such implementations might re-configure (foreseeing or noting changes in medical/health, legal, or manufacturing work, for example). This paper turns our attention to the various forms of labor that must be undertaken to conceive of, train/test, deploy, and ongoingly maintain AI/ML systems in practice. In particular, this paper draws on an ongoing ethnographic endeavor in a large, global technology and consulting corporation and leverages a work practices lens to examine three themes: curating datasets (everyday work practices of data pre-processing); tending models (everyday work practices of training, deploying, and maintaining predictive models); and configuring compute (everyday work practices of back-end infrastructuring, commonly called “DevOps”). This paper considers the value of a work practices lens in studying contemporary sociotechnical labor ecosystems. By locating the work practices through which AI/ML systems emerge, this paper shows that these technologies indeed require considerable human labor, at the same time they are often talked about as drivers of automation and displacers of work. This extends discourses around the “future of work,” giving light to the various standpoints and experiences of labor such imaginaries implicate and ongoingly re-configure.

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Notes

  1. 1.

    In an unsupervised approach, by contrast, the algorithmic modelling techniques take only input and devise their own outputs; unsupervised approaches are often used in exploratory data analysis.

  2. 2.

    Agile stands in contrast to more traditional methods of organizing software engineering work, such as the Waterfall Method, where user requirements are gathered and analyzed at the beginning of a project (top of the Waterfall) before development efforts proceed. Once the project progress to the subsequent phase (i.e., moving from requirements analysis to development work) it does not revert back to prior phases (i.e., water falls in one direction).

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Acknowledgements

Thank you to informants and collaborators for sharing their time, insights, and points-of-view. All opinions are my own and do not reflect any institutional endorsement.

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Authors and Affiliations

  1. IBM Research – Almaden, San Jose, CA, USA

    Christine T. Wolf

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  1. Christine T. Wolf
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Correspondence to Christine T. Wolf .

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Editors and Affiliations

  1. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Anneli Sundqvist

  2. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Gerd Berget

  3. University of Boras, Boras, Sweden

    Jan Nolin

  4. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Kjell Ivar Skjerdingstad

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Wolf, C.T. (2020). AI Models and Their Worlds: Investigating Data-Driven, AI/ML Ecosystems Through a Work Practices Lens. In: Sundqvist, A., Berget, G., Nolin, J., Skjerdingstad, K. (eds) Sustainable Digital Communities. iConference 2020. Lecture Notes in Computer Science(), vol 12051. Springer, Cham. https://doi.org/10.1007/978-3-030-43687-2_55

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  • DOI: https://doi.org/10.1007/978-3-030-43687-2_55

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  • Print ISBN: 978-3-030-43686-5

  • Online ISBN: 978-3-030-43687-2

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