Study of Private Sector Research and Data Sharing Practices
Activities within companies in the areas of artificial intelligence, machine learning, and behavioral analytics implies new trends in internal user research, but lack empirical study on how these new roles are developing and impacting the existing research and development (R&D) ecosystem. Data scientists and social scientists employed by tech companies are building research divisions where the line between practice (direct product improvement) and research may be blurry. Further, these private sector data are of rich interest to academics, and gaining access for a study is an exception instead of the rule.
These shifts in sites of research and sources of large repositories may challenge fundamental tenets of the research ecosystem such as the free flow of information via publications and presentations, access to data for validation, and ethics frameworks dependent upon the academic model. Additionally there is little understanding of public attitudes regarding corporate research use of user data, and how these evolving norms may influence the acceptability of particular practices.
This talk will cover preliminary findings from an ongoing dissertation study using a mixed method approach of both qualitative interviews with research practitioners and quantitate survey work of user attitudes. The aim of this dissertation is to inform national and corporate R&D policies.
Elaine Sedenberg is a Ph.D. candidate at the UC Berkeley School of Information and co-director of the Center for Technology, Society & Policy (CTSP). Previously she was a science policy fellow at the Science and Technology Policy Institute (STPI) in Washington, D.C., and has many years experience working in federal S&T policy as well as technology transfer activities.