Por Casey Fiesler (University of Colorado Boulder).
Abstract: Big data has opened up new possibilities and transformed the ways we conduct research in nearly every discipline. However, ethical considerations and education for research has long focused on human subjects, governed in the U.S. by institutional review boards. Data science often falls through the cracks of these regulations, so it is even more imperative that we have strong ethical norms and guidelines. This starts with the reminder that though data scientists may not interact directly with people, the data collected and analyzed very often comes from people, which opens up important considerations around privacy, consent, and harm. Moreover, applications of data science research, particularly with respect to prediction, have the potential for large-scale societal impacts. In considering the broad landscape of technology ethics when it comes to uses and applications of big data, I will argue for a fundamental shift in how we teach ethics to future data scientists and researchers.
Bio: Casey Fiesler is an assistant professor and founding faculty of information science at University of Colorado Boulder, where she researches and teaches in the areas of technology ethics, internet law and policy, and online communities. Her work on research ethics for data science, ethics education in computing, and broadening participation in computing is supported by the National Science Foundation, as well as Mozilla and Omidyar Network as part of the Responsible Computer Science Challenge. She holds a PhD from Georgia Tech in Human-Centered Computing and a JD from Vanderbilt University Law School.
