Does social good justify risking personal privacy?

When data-driven improvements involve personally identifiable data, or even data that can be used to infer sensitive information about individuals, we face the dilemma that we potentially risk compromising privacy. As we see increased emphasis on using data mining to effect improvements in a range of socially beneficial activities, from improving matching of talented students to opportunities for higher education, or improving allocation of funds across competing school programs, or reducing hospitalization time following surgery, the dilemma can often be especially acute. The data involved often is personally identifiable or revealing and sensitive, and many of the institutions that must be involved in gathering and maintaining custody of the data are not equipped to adequately secure the data, raising the risk of privacy breaches. How should we approach this trade-off? Can we assess the risks? Can we control or mitigate them? Can we develop guidelines for when the risk is or is not worthwhile, and for how best to handle data in different common scenarios? Chairs Raghu Ramakrishnan and Geoffrey I. Webb bring this panel of leading data miners and privacy experts together to address these critical issues.