David Menager

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.