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2025Journal Paper
arxiv Participatory AI: A Scandinavian Approach to Human-Centered AI ↗
Caroline BergerClick to read abstract
AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly generic. The algorithmic automation they provide threatens human agency and democratic values in both workplaces and daily life. To confront such challenges, we turn to Scandinavian Participatory Design (PD), which was devised in the 1970s to face a similar threat from mechanical automation. In the PD tradition, technology is seen not just as an artifact, but as a locus of democracy. Drawing from this tradition, we propose Participatory AI as a PD approach to human-centered AI that applies five PD principles to four design challenges for algorithmic automation. We use concrete case studies to illustrate how to treat AI models less as proprietary products and more as shared socio-technical systems that enhance rather than diminish human agency, human dignity, and human values.
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Proceedings of the 14th Annual Workshop on the Intersection of HCI and PL (PLATEAU 2024) • 2024Conference Paper
Scientists and Code: Programming as a Tool ↗
Caroline BergerClick to read abstract
Many scientists use programming to analyze their data. In this paper, we explore the computational ecosystem of scientists and their socio-technical system of computing through a human-centered approach. By employing contextual inquiry techniques with nine scientists drawn from fields such as theoretical physics, biomedical science, and entomology, we learned that programming is a tool for scientists, and as such the output is more important than the code itself. We found that during analysis, scientists often write code to create plots, and then compare these plots to assess the match of output to their expectation. Participants used ChatGPT while coding. We also found that scientists' programming tools and practices often limit their analysis. Finally, based on a combined human-computer interaction and programming language analysis, we identify drivers and blockers of scientists' work. Our findings uncover opportunities for the design of programming tools and languages.