Guiding the specification of sociotechnical Machine Learning systems
Addressing vulnerabilities and challenges in Machine Learning practice
A.E. Wolters (TU Delft - Technology, Policy and Management)
Marijn Janssen – Graduation committee member (Information and Communication Technology)
R.I.J. Dobbe – Mentor (Information and Communication Technology)
F.S. Gürses – Graduation committee member (TU Delft - Organisation & Governance)
Nick Jetten – Mentor
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Abstract
There is a need for a more comprehensive sociotechnical systems view on ML. Such a view looks at the development and use of an ML system in practice as being a sociotechnical ML system: "a system consisting of technical artefacts, human agents and institutions, in which a machine-based subsystem influences its real or virtual environment by automating, supporting or augmenting decision-making". This research takes on this view to design a sociotechnical guide for ML practice, centring the specication of sociotechnical ML systems. Taking on the guidelines contributes to a safe and effective development and use of ML systems.