Eric Horvitz and Deirdre Mulligan
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.
At Science online:
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Science 17 July 2015:
Vol. 349 no. 6245 pp. 253-255
DOI: 10.1126/science.aac4520
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