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 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 eleven types of challenges, including those which are technical, societal, and humanist, providing example problems and suggesting ways to address some of the unanticipated consequences of Big Data.
Alfred Spector is Chief Technology Officer at Two Sigma, a firm dedicated to using information to undertake many forms of economic optimization. Dr. Spector’s career has led him from innovation in large scale, networked computing systems (at Stanford, CMU, and his company, Transarc) to broad research leadership: eight years leading Google Research and five years leading IBM Software Research. Recently, Spector has lectured widely on the growing importance of computer science across all disciplines (CS+X) and on the Societal Implications of Data Science. He received an AB in Applied Mathematics from Harvard and a Ph.D. in Computer Science from Stanford. He is a Fellow of the ACM and IEEE, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing and was co-awarded the 2016 ACM Software Systems Award.