Data-driven approaches have led to powerful prediction, optimization and automation techniques. Powered by large-scale, networked computer systems and machine learning algorithms, these have been very impactful and hold great promise in many disciplines, even humanities and social sciences. However, no new technology arrives without complications, and we have recently seen the press and various political circles illustrating problematic implications of Big Data.

This presentation by Alfred Z. Spector of Two Sigma aims to balance the opportunities provided by Big Data and its associated artificial intelligence techniques with a discussion of the various challenges that have ensued, reviewing 11 types of challenges, including those that are technical, societal, and humanist, providing example problems and suggesting ways to address some of the unanticipated consequences of Big Data.