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.
Publications
Abstracs
Netta Avnoon & Amalya Oliver
Abstracs
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.
Abstracs
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decision-making process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.
