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Lecture: "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy"

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Location: Carey Auditorium, Hesburgh Libraries

2018 Cathy O Neil Headshot

Author of the New York Times bestseller, Weapons of Math Destruction, mathematician and data skeptic, Cathy O’Neil, uncovers the dark secrets of big data, showing how our “objective” algorithms could in fact reinforce human bias. Algorithms can embed bias, they can propagate or even exacerbate inequality, or they can just be plain inaccurate. How do we keep track of all the potential problems? How do we make sure the algorithms we build “work well”? What do we even mean by that? In this talk Cathy O’Neil will introduce the ethical matrix, a construction borrowed from moral philosophy, as a way of organizing our thoughts around important and urgent questions like these. In her thought provoking and humorous way, Cathy will discuss how big data and analytics impact all aspects of life, from scoring teachers and students, sorting résumés, granting (or denying) loans, evaluating workers, targeting voters, setting parole, and monitoring our health.

Cathy O’Neil earned a Ph.D. in math from Harvard, was a postdoctoral fellow in the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then switched over to the private sector, working as a quantitative analyst for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. She left finance in 2011 and started working as a data scientist in the New York start-up scene, building models that predicted people’s purchases and clicks. She founded ORCAA, an algorithmic auditing company, and is the author of “Weapons of Math Destruction.”
 
Organized by the MS-ACMS: Data Science program, the event is co-sponsored by the Mendoza College of Business and the Office of the Provost. This is a free event and is open to all. 
 

Originally published at conductorshare.nd.edu.