Axes for Sociotechnical Inquiry in AI Research

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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.

What you need to know:

Following on from our previous work studying three new subfields of human-facing AI research, we present four axes for sociotechnical inquiry into AI:

  1. Value: What are the nature and metrics of value?
  2. Optimization: What is the relationship between the systemand the broader environment dynamics?
  3. Consensus: What are methods for reaching agreement onrisk and who is included in the process?
  4. Failure: Is failure inevitable, how is it contained, and howis it measured?
Axes of Comparison for Sociotechnical Inquiry. These terms serve to highlight essential properties of each subfield along the set of axes we propose. We elaborate and explore related properties in Section 3.


 author={Dean, Sarah and Gilbert, Thomas Krendl and Lambert, Nathan and Zick, Tom},
 journal={IEEE Transactions on Technology and Society},
 title={Axes for Sociotechnical Inquiry in AI Research},