Algorithmic Decision-Making in Difficult Scenarios

Authors

  • Christopher B. Rauch Drexel University
  • Ursula Addison Parallax Advanced Research
  • Michael Floyd Knexus Research Corporation
  • Prateek Goel Drexel University
  • Justin Karneeb Knexus Research Corporation
  • Ray Kulhanek Parallax Advanced Research
  • Othalia Larue Parallax Advanced Research
  • David Ménager Parallax Advanced Research
  • Mallika Mainali Drexel University
  • Matthew Molineaux Parallax Advanced Research
  • Adam Pease Parallax Advanced Research
  • Anik Sen Drexel University
  • Jt Turner Knexus Research Corporation
  • Rosina Weber Drexel University

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31285

Keywords:

Algorithmic Decision-making, Explainable Case-Based Reasoning (ECBR), Counterfactual Analysis, Heuristic Decision Strategies, Event-Based Diagnosis, Decision Justifications, Subjective Decision Attributes

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.
AAAI Spring Symposium 2024 Proceedings Cover

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Published

2024-05-20

How to Cite

Rauch, C. B., Addison, U., Floyd, M., Goel, P., Karneeb, J., Kulhanek, R., … Weber, R. (2024). Algorithmic Decision-Making in Difficult Scenarios. Proceedings of the AAAI Symposium Series, 3(1), 583–585. https://doi.org/10.1609/aaaiss.v3i1.31285

Issue

Vol. 3 No. 1: Proceedings of the 2024 AAAI Spring Symposium Series

Section

Symposium on Human-Like Learning