Christopher B. Rauch, Ursula Addison, Michael Floyd, Prateek Goel, Justin Karneeb, Ray Kulhanek, O. Larue, David Ménager, Mallika Mainali, Matthew Molineaux, Adam Pease, Anik Sen, Jt Turner, Rosina Weber
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Algorithmic Decision-Making in Difficult Scenarios
We present an approach to algorithmic decision-making that emulates key facets of human decision-making, particularly in scenarios marked by expert disagreement and ambiguity. Our system employs a case-based reasoning framework, integrating learned experiences, contextual factors, probabilistic reasoning, domain-specific knowledge, and the personal traits of decision-makers. A primary aim of the system is to articulate algorithmic decision-making as a human-comprehensible reasoning process, complete with justifications for selected actions.
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