Perspectives on Big Data, Ethics, and Society
Jacob Metcalf, Emily F. Keller, Danah Boyd
The Council for Big Data, Ethics, and Society was convened to bring together researchers from diverse fields who were thinking deeply about ethical, social and policy challenges associated with the rise of “big data” research and industry, with an eye toward developing recommendations about future directions for the field.
Our reports, meetings, and ongoing conversations have consistently indicated that there is a disjunction between the familiar concepts and infrastructures of science and engineering, on the one hand, and the epistemic, social, and ethical dynamics of big data research and practice, on the other. We contend that facilitating ethical conduct in data science and related endeavors requires careful consideration of big data’s broad technical, social, and political contexts. Big data is marked by technical advances in storage capacity, speed, and price points of data collection and analysis, and by a move towards understanding data as continuously collected, almost-infinitely networkable, and highly flexible. The ability to analyze datasets from highly disparate contexts and generate new, unanticipated knowledge sets the stage for both the power and peril of big data research and broader data science-related analysis.
The conceptual, regulatory, and institutional resources of research ethics developed over the last 70 years were premised on assumptions about human data research practices that sometimes do not easily apply to data analytics work done under the umbrella of big data. This results in conflicts over whether big data research methods should be excluded from or forced to meet existing norms, whether existing norms should be made to accommodate the special circumstances of big data, or whether entirely new norms and institutional commitments are needed.
The Council’s findings, outputs, and recommendations—including those described in this white paper as well as those in earlier reports—address concrete manifestations of these disjunctions between big data research methods and existing research ethics paradigms. We have identified policy changes that would encourage greater engagement and reflection on ethics topics. We have indicated a number of pedagogical needs for data science instructors, and endeavored to fulfill some of them. We have also explored cultural and institutional barriers to collaboration between ethicists, social scientists, and data scientists in academia and industry around ethics challenges. Overall, our recommendations are geared toward those who are invested in a future for data science, big data analytics, and artificial intelligence guided by ethical considerations along with technical merit