Weston Roundtable Lecture - Bill Howe

Data too sensitive to be "open" typically remains "closed" as proprietary information. This dichotomy undermines efforts to make algorithmic decision systems more fair, transparent, and accountable. In this talk, I’ll describe the algorithms we’re developing to generate privacy-preserving and bias-corrected synthetic datasets, and touch on the legal protections that govern their use. The full abstract can be found via the URL.