Standard: Collaborative Research: Emerging Cultures of Data Science Ethics in the Academy and Industry

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The 'big data' era has created new scientific roles in the form of 'data scientists' - specialized information knowledge workers who apply technical and research skills to derive knowledge from large-scale databases. Concomitant with the creation of these new roles, however, has been the rise of high-profile controversies around the potential abusive use of big data and of the algorithms whose creation they enable. This project thus focuses on 'data science ethics' and works to understand how to best cultivate a culture of ethics in data science. It will assess the state, structure, and substance of data ethics in both educational and industrial contexts. Within the academic sector, the project will conduct interviews with faculty and undergraduates and analyze relevant syllabi to develop an account of what constitutes data ethics in the academy. Comparing these data across faculty and disciplines will show major areas of agreement and disagreement over what data ethics education should entail. Within the industrial sector, the project will research how data ethics are beginning to figure into corporate practice and study how companies define data ethics and attempt to integrate ethical considerations into everyday work. Data will come from in-depth interviews with those in industry and analyses of corporate documents from companies that have made public commitments to addressing data ethics. The PIs would then compare data and findings from across these two contexts to identify commonalities, differences, and useful strategies for advancing data ethics across social contexts and professional sectors. This project will draw together a diverse group of researchers who in collaboration with educators and data science practitioners will: (1) document and assess barriers and opportunities for integrating ethics into data science practice; (2) assess the continuities and discontinuities emerging between industrial and academic contexts; and (3) develop a foundation for cohesive, comprehensive, integrative data ethics education. The investigators will use a range of complementary methods to do so, including qualitative interviews, expert judgement, and quantitative computational analyses of the latent thematic and topical structure of documents from academia and industry. Project outcomes and findings will be disseminated through conferences, journals, and through holding a synthetic workshop after the completion of data collection and analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.