title: Policy forum. Data, privacy, and the greater good.
abstract: Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and wellbeing. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and to society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation.
authors.affiliation: Microsoft Research, Redmond, WA 98052, USA. horvitz@microsoft.com dmulligan@berkeley.edu.
authors.forename: Eric
authors.initials: E
authors.lastname: Horvitz
authors.affiliation: University of California, Berkeley, Berkeley, CA 94720, USA. horvitz@microsoft.com dmulligan@berkeley.edu.
authors.forename: Deirdre
authors.initials: D
authors.lastname: Mulligan
mesh_terms: D035041:Anonymous Testing / Q000941:ethics* / Q000331:legislation & jurisprudence / Q000379:methods; D001185:Artificial Intelligence / Q000941:ethics* / Q000331:legislation & jurisprudence; D019052:Depression, Postpartum / Q000175:diagnosis / Q000517:prevention & control; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D011050:Policy Making; D018907:Privacy; D060756:Social Networking