Axes for Sociotechnical Inquiry in AI Research

TL;DR

This paper proposes four key axes—value, optimization, consensus, and failure—for sociotechnical inquiry in AI research, aiming to better understand and mitigate societal impacts of AI technologies.

Contribution

It introduces a structured lexicon and framework for sociotechnical analysis in AI, addressing gaps in tools for external investigation and cross-disciplinary understanding.

Findings

Introduces four axes for sociotechnical inquiry: value, optimization, consensus, failure.

Provides a lexicon to facilitate sociotechnical analysis in AI research.

Illustrates the framework with an example of consumer drone technology.

Abstract

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.