- IBS Curriculum
- Innovation and the Value of Privacy
- Growth for Entrepreneurs
- Can My Company Change?
- Business and Politics
- Small Worlds of Governance
- Bolder Policies for Diversity?
- Governance and Compensation
- The Quantitative Revolution
- Inclusive Leadership
- Preventing the Next Crisis
- Universities and Women
- The Benjamin Botwinick Prizes in Business Ethics
- The Paul M. Montrone Seminar Series on Ethics
- The KPMG Peat Marwick / Stanley R. Klion Forum
- Annual Leadership Conference
- Bernstein Debates
- Diversity and Inclusion for All
- Events Calendar
Hosted by The Sanford C. Bernstein & Co. Center for Leadership and Ethics and the Data Science Institute at Columbia University.
In 1999, Scott McNealy, then CEO of Sun Microsystems, reportedly said about the new age of internet connectivity: “You have zero privacy anyway. Get over it.” However many fear that individuals have largely lost the option of anonymity. While customers value their privacy, they give it away or trade it at low value in practice. Companies are able to monetize the value of this private information because they can aggregate, analyze this information for their use and sell and trade it to third parties. Meanwhile, customers routinely fail to understand the confusing privacy clauses in online licenses and have difficulty in knowing if the big data predictions about them are biased, accurate, or fair.
On February 5th, 2016, the Data Science Institute and the Sanford C. Bernstein & Co. Center for Leadership and Ethics at Columbia University hosted a research-inspired conference to answer a few fundamental questions about privacy, big data, and predictive modeling to turn this situation around. Some questions addressed were: How can we use data to improve privacy for individuals? Can we tell how companies are using our data and which ones are offering better protection? Do government agencies, such as the FTC or the Bureau of Financial Protection, have any impact on improving individual privacy? Have researchers or entrepreneurs proposed solutions to improving data protection for customers?
|8:30 – 8:55 AM||
Breakfast and Registration
|9:00 – 9:10 AM||
Welcome Remarks and Introduction
|9:10 – 9:45 AM||
|10:35AM– 10:55 AM||
|10:55 – 11:45 AM||
|11:45 AM– 12:30 PM||
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Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence, Mathias Lecuyer, Riley Spahn, Yannis Spiliopoulos, Augustin Chaintreau, Roxana Geambasu, and Daniel Hsu
Considering Privacy in Predictive Modeling Applications, KKD, 2014. Troy Raeder, Brian Dalessandro, Claudia Perlich