Recently, whenever I am talking to my fellow data librarians, it is not long before someone brings up Cathy O’Neil’s new book Weapons of Math Destruction : How Big Data Increases Inequality and Threatens Democracy.
Alarmingly, closed-source algorithms are playing an increasing role in social policy. O’Neil examines some of the (bad) data used as inputs into these algorithms and the problematic assumptions made by their creators (e.g., predictive policing based on arrest records not crime reports, recidivism scores for first-time criminal sentencing, teacher value-added scores used to fire educators).
Clay Shirky, in the New York Times Book Review1, sums it up well:
O’Neil’s book offers a frightening look at how algorithms are increasingly regulating people… Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data… [She] does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives.
The library has ordered a copy, but it has not yet arrived. If you want to join the conversation now, I recommend you check out the author’s book talk presented at Data & Society‘s Databite (embedded below).
1Shirky, C. (2016, Oct 09). WEAPONS OF MATH DESTRUCTION how big data increases inequality and threatens democracy. New York Times Book Review, , 34-BR.34. Retrieved from http://search.proquest.com/docview/1826909139?accountid=13475