Newsroom
Algorithmic Fairness
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FDS Colloquium: Cynthia Dwork (Harvard), “Outcome Indistinguishability and its Diverse Applications”
Abstract: Outcome Indistinguishability, a notion from algorithmic fairness with roots in complexity theory, frames learning not as loss minimization – the dominant paradigm in supervised […]
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FDS Colloquium: Bhramar Mukherjee (Yale), “Analysis of “Big” Real-World Health Care Data: Promises and Perils”
Speaker: Bhramar Mukherjee, Ph.D.Senior Associate Dean of Public Health Data Science and Data EquityAnna M.R Lauder Professor of BiostatisticsProfessor of Epidemiology (Chronic Diseases) and […]


