JPM Distinguished Lecture Series in AI, Tuesday, October 29, 2020
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 to-date and hold great promise in many disciplines, in finance, but even the humanities. However, no new technology arrives without complications, and we have recently seen the press and various political circles illustrating real, potential, and fictional implications of Big Data.
This presentation aims to balance the opportunities provided by Data Science and its associated artificial intelligence techniques with a discussion of the various challenges that have ensued. I review eleven types of challenges, including those which are technical (resilience and complexity), societal (difficulties in setting objective functions or understanding causation), and humanist (issues relating to free will or privacy). I build on my experiences in finance and big technology, show example problems, and suggest ways to address some of the unanticipated consequences of Big Data.