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Algorithmic Decision-Making in Difficult Scenarios
Conference proceeding   Open access

Algorithmic Decision-Making in Difficult Scenarios

Christopher B. Rauch, Ursula Addison, Michael Floyd, Prateek Goel, Justin Karneeb, Ray Kulhanek, Othalia Larue, David Menager, Mallika Mainali, Matthew Molineaux, …
PROCEEDINGS OF THE 2024 AAAI SPRING SYMPOSIUM SERIES, VOL 3 NO 1, v 3(1), pp 583-585
21 May 2024
DOI: https://doi.org/10.1609/aaaiss.v3i1.31285
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https://doi.org/10.1609/aaaiss.v3i1.31285View
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Abstract

Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Computer Science, Theory & Methods Science & Technology Computer Science Technology
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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