Academic Publications

How do data scientists frame their relations with domain experts? This study focuses on data scientists’ aspired professional jurisdiction and their multiple narratives regarding data science’s relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding their relations with domain experts: (a) replace experts, (b) absorb experts’ knowledge, and (c) provide a service to experts. These three narratives construct data scientists’ expertise as universal and omnivorous; namely, they are relevant to many domains and allow data scientists to be flexible in their claim for authority.

This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI’s disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both conservative and innovative in their relations to it: accept it, subordinate it, reject it and surrender to it, all the same time. These perspectives are: (a) to integrate AI tools and skills into the radiology profession by cooperating and coproducing with AI experts while preserving the core values and structures of the radiology profession; (b) to absorb AI into radiology as (yet another) technology, subordinating it to radiologists’ authority; (c) to fight-off the threat made by AI by undermining the legitimacy and capabilities of AI in radiology and strengthening professional boundaries and (d) to assimilate the radiology profession into the field of AI. These perspectives enable radiologists as a powerful medical specialty to engage in a rhetorical dance with the equally powerful AI specialty and challenge techno-optimistic approaches to innovation.