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Using Bayesian Networks to Analyze What Experts Need to Know (and When they Know Too Much)

Presented by: 
William Thompson University of California, Irvine
Thursday 29th September 2016 -
16:15 to 17:00
INI Seminar Room 1
What is the proper evidentiary basis for an expert opinion? When do procedures designed to reduce bias (e.g., blinding, sequential unmasking) hide too much from the expert; when do they hide too little? When will an expert’s opinion be enhanced, and when might it be degraded or biased, by the expert’s consideration of contextual information? Questions like this are important in a variety of domains in which decision makers rely on expert analysis or opinion. This presentation will discuss the use of conditional probabilities and Bayesian networks to analyze these questions, providing examples from forensic science, security analysis, and clinical medicine. It will include discussion of the recommendations of the U.S. National Commission on Forensic Science on determining the “task-relevance” of information needed for forensic science assessments. 
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons