Algorithmic Decision-Making in Difficult Scenarios

Christopher B. Rauch, Ursula Addison, Michael W. Floyd, Prateek Goel, Justin Karneeb, Ray Kulhanek, Othalia Larue, David H. Ménager, Mallika Mainali, Matthew Molineaux, Adam Pease, Anik Sen, J. T. Turner, Rosina Weber

Abstract: 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.