This poster is part of the OR2020 Virtual Poster Session which takes place in the week of June 1-5. We encourage you to ask questions and engage in discussion on this poster by using the comments feature. Authors will respond to comments during this week.
Maria Esteva, Sharon Strover, Soyoung Park, Christopher Rossbach, John Thywissen
Open datasets are at the core of countless AI applications. However, the complexities involved in large data aggregations, transformations, distribution and reuse, and the limited capacity to validate ethical implications embedded in routine data practices, make it difficult to track and prevent breaches. Recognizing that data and the systems that manage it are not neutral but entangled in a chain of decisions, organizational priorities, technical conditions, and social norms, we investigate how ethical data management can be a point of departure for designing and evaluating AI applications. Our research suggests an array of issues and decision-making instances that touch ethics data management at each lifecycle stage. The findings can inform open repositories’ policies and curation practices towards ethical open data for use in responsible AI. In this poster we describe our research methods using the case of natural hazards data.
(Page through the slides below and click on the full screen window)
About the presenter:
Sharon Strover is a Professor at the School of Journalism, University of Texas at Austin. She also direct the Technology & Information Policy Center. Her research and teaching focus on communication technologies and their policy implications.