Axes for Sociotechnical Inquiry in AI Research
It addresses the problem of insufficient sociotechnical inquiry in AI research for researchers and policymakers, offering a framework for cross-disciplinary analysis, but is incremental as it builds on existing sociotechnical concepts.
The paper tackles the gap between AI development and understanding its societal impacts by proposing four inquiry directions—value, optimization, consensus, and failure—to analyze technologies like consumer drones, providing a lexicon for sociotechnical analysis.
The development of artificial intelligence (AI) technologies has far exceeded the investigation of their relationship with society. Sociotechnical inquiry is needed to mitigate the harms of new technologies whose potential impacts remain poorly understood. To date, subfields of AI research develop primarily individual views on their relationship with sociotechnics, while tools for external investigation, comparison, and cross-pollination are lacking. In this paper, we propose four directions for inquiry into new and evolving areas of technological development: value--what progress and direction does a field promote, optimization--how the defined system within a problem formulation relates to broader dynamics, consensus--how agreement is achieved and who is included in building it, and failure--what methods are pursued when the problem specification is found wanting. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.