A Proof-of-Concept Validation of Alignment in Decision-Making Attributes for Trustworthy AI
Amy L. Summerville, Louis Martí, Ion Juvina, B. Locke Welborn, Cara Widmer, Alice Leung · 2025
Aligning AI systems to the attributes of human decision-makers offers a promise of greater AI trustworthiness. The current research offers a proof-of-concept experiment investigating whether alignment to six different high-level attributes of decision-making in critical care medical triage and military field medicine is related to greater trust in more aligned target decision-makers. We find that greater alignment on these values is associated with reports of the target as trustworthy, with ratings that the target makes decisions in the same way as the participant, and with willingness to delegate to the target. Additionally, alignment predicted a forced-choice decision to delegate to one of two target decision makers. This research thus offers evidence that creating AI systems capable of alignment on these characteristics could offer an approach for trustworthy AI in medical triage.