Mortal vs. Machine: A Compact Two-Factor Model for Comparing Trust in Humans and Robots

Trust in robots is often analyzed with scales built for either humans or automation, making cross-species comparisons imprecise. Addressing that gap, this paper distils decades of trust scholarship, from clinical vs. actuarial judgement to modern human–robot teaming, into a lean two-factor framework: Mortal vs. Machine (MvM). We first surveyed classic technology-acceptance and automation-reliance research and then integrated empirical findings in human–robot interaction to identify diagnostic cues that can be instantiated by both human and machine agents. The model includes (i) ability—perceived task competence and reliability—and (ii) value congruence—alignment of decision weights and trade-off priorities. Benevolence, oft-included in trust studies, was excluded because current robots cannot manifest genuine goodwill and existing items elicit high dropout. The resulting scale travels across contexts, allowing for researchers to benchmark a robot against a human co-worker on identical terms and enabling practitioners to pinpoint whether performance deficits or priority clashes drive acceptance. By reconciling anthropocentric and technocentric trust literature in a deployable diagnostic, MvM offers a field-ready tool and a conceptual bridge for future studies of AI-empowered robotics.