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Artificial Intelligence’s new clothes? A system technology perspective

In this paper, we offer an original framework to study Artificial Intelligence (AI). The perspective we propose is based on the idea that AI is a system technology, and that a useful description of AI cannot abstain from mapping the components of the system, their interdependence, and how the synergies they create shape at the roots the directions of AI development. We adopt the concept of Large technical systems (LTS) to give substance and structure to our idea. Using LTS, we are able to scaffold AI and the forces at work steering its production, deployment, and evolution. We find that AI as a system shares essential features with infrastructural technologies such as the Internet. The LTS framework proves very useful to capture important nuances of the technology, and it allows us to trace the connections and cross–influences among its constituting domains—algorithms (software), compute (hardware), and data. We compare our proposed framework with other concepts usually associated with radical innovations, and suggest in which respects AI differs from these ideal–types. We consider ours a timely exercise, as we witness the formation of an AI industry. While in the making, this industry is rapidly ossifying, together with its specific problems, power imbalances, and development scenarios; the focus on the system–ness of AI allows uncovering the deeper structure of this technological breakthrough.

Reforming work patterns or negotiating workloads? Exploring alternative pathways for digital productivity assistants through a problematization lens

Digital trace data can be used to capture organizational practices in granular detail and enable the automation of a wide range of managerial tasks. One example is Digital Productivity Assistants (DPA) that harness digital trace data about knowledge workers’ performance and make targeted suggestions for how to improve and optimize their work patterns. Previous research shows that despite benevolent intentions to increase workers’ wellbeing, DPA tend to introduce novel forms of exploitation and control. Inspired by Michel Foucault’s philosophical strategy of ‘problematization,’ which emphasizes how practices are constructed in the form of problems that subsequently shape certain solutions, this paper takes a critical yet constructive view of DPA. Specifically, we conduct a genealogical reading of the DPA tool, Microsoft MyAnalytics, to investigate the problematics that have structured its emergence, as well as how its uses imply certain discursive commitments to philosophical and ethical questions. In the prevailing discourse, DPA cast digital trace data as a learning opportunity and thereby commit to individualizing the responsibility for handling the paradoxical nature of increasingly fluid work arrangements. Conversely, in our account of the history of MyAnalytics, we uncover a ‘lost discourse’ committed to trace data as a resource that can help knowledge workers negotiate excessive workloads. We propose the problematization lens as a way critically to articulate alternatives and speculate about instantiations of digital technology that today seem ‘unthinkable’.