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Short abstract:

This paper addresses the lack of ‘appropriate’ data to train AI machines in many sectors, and examines the experiences of practitioners who are under pressure to prioritise feeding the machine with more and better data.

Long abstract:

Given the growing hype about ‘AI’ futures, more in-depth empirical research is needed to critically examine the limits and barriers to AI’s expansion in actual contexts of practice. One such limit in many sectors is the availability of appropriate data to train AI machines.

Previous research identifies a deepening “desire for numbers” (Kennedy, 2016), or data outputs, including predictions (Mackenzie, 2015), that can contribute to the stabilisation and legitimisation of knowledge claims (Fine 2007; Daipha 2015; Heymann et al, 2017). However, there has been minimal attention on the pressure to feed data into the systems that produce these numbers. That is, there has been less attention paid to the struggle to generate inputs.

A focus on inputs brings our attention to how practitioners are experiencing, and in some cases resisting, workplace pressures to ‘feed the machine’ - a term we use to allude to the data and other resources required to sustain both the AI and capitalist systems within which they are embedded.

We draw on empirical research which consists of interviews, focus groups and observations with 65 UK-based practitioners in the pharmaceutical industry, Higher Education and arts practice. Our analysis shows that AI machines are not always and straightforwardly fed abundant and appropriate data. We argue that, in some contexts of practice, efforts to develop AI in the face of such barriers lead to increased pressures on practitioners to prioritise feeding the machine with more and better data, which has implications for workplace cultures of AI practice.