Abstract
This paper considers everyday software engineering (SE) work practices in applied artificial intelligence (AI) and machine learning (ML) development projects. As contemporary AI/ML systems become increasingly embedded into a wide array of everyday digital services, a number of concerns emerge over the opaque and “black box” constitution of these technical capabilities. How do software developers come to understand complex AI/ML capabilities and how do they explain their inner-workings to others? This paper empirically investigates these questions by drawing on an ethnographic study of AI/ML research and development at a large, global technology and services company, highlighting the situated, careful labor involved in configuring access around AI/ML systems and services. By rendering these situated practices visible, this paper contributes important provocations for current discourses on AI explainability (XAI) and interpretability: rather than a quality or component of a model, system, or interface, access emerges through ongoing, unfolding, and effortful sociotechnical encounters.
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Thank you to informants and collaborators – your time, experiences, and insights have been a gift and are invaluable to this work. All opinions expressed herein are my own and do not reflect any institutional endorsement.
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Wolf, C.T. (2020). Picking Apart the Black Box: Sociotechnical Contours of Accessibility in AI/ML Software Engineering. In: Spohrer, J., Leitner, C. (eds) Advances in the Human Side of Service Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1208. Springer, Cham. https://doi.org/10.1007/978-3-030-51057-2_28
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DOI: https://doi.org/10.1007/978-3-030-51057-2_28
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