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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. [ABSTRACT FROM AUTHOR]

Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scientists, we ask: how do data scientists understand, interpret, and depict algorithmic ethics? And what ideologies, discourses, and worldviews shape algorithmic ethics? Our findings point to three dominant moral logics: (1) ethics as a personal endeavor; (2) ethics as hindering progress; and (3) ethics as a commodity. We show that while data science is a nascent profession, these moral logics originate from the techno-libertarian culture of its parent profession—engineering. Finally, we discuss the potential of these moral logics to mature into a more formal, agreed-upon moral regime. [ABSTRACT FROM AUTHOR]