Opportunities and Perils in Data Science
Dr. Alfred Z. Spector
Presentations at Berkeley, Caltech, Cornell, Harvard, MIT, and Rice
Over the last few decades, empiricism has become the third leg of computer science, adding to the field’s traditional bases in mathematical analysis and engineering. This shift has occurred due to the sheer growth in the scale of computation, networking, and usage as well as progress in machine learning and related technologies. Resulting data-driven approaches have led to extremely powerful prediction and optimization techniques and hold great promise, even in the humanities and social sciences. However, no new technology arrives without complications. In this presentation, I will balance the opportunities provided by big data and associated A.I. approaches with a discussion of the various challenges. I’ll enumerate ten plus one categories including those which are technical (e.g., resilience and complexity), societal (e.g., difficulties in setting objective functions or understanding causation), and humanist (e.g., issues relating to free will or privacy). I’ll provide many example problems, and make suggestions on how to address some of the unanticipated consequences of big data.