Philip Porwol

Anthropomorphizing artificial intelligence (AI) -- that is, attributing human characteristics to it -- is prevalent in public discourse. Using human-like voices or appearances is a prominent example. Similarly, the language used to describe AI applications is anthropomorphic. AI is "fed" with data, it "learns", "acts", and "decides". However, little is known about the effects of using such anthropomorphisms when describing AI. Previous research has shown that anthropomorphic appearance and design increase trust among people interacting with AI applications. To test whether this translates to anthropomorphism used in descriptions of AI, we conducted two pre-registered experiments comparing two conditions in which an AI application is either linguistically anthropomorphized or not. In the first experiment, participants read a fictitious newspaper article about the usage of an AI application in radiology. In the second experiment, participants completed a classification task using AI. Using a response-elicitation approach, participants had to judge their trust and distrust towards the AI system, and reliance was compared in the second experiment. We found no significant differences in trust, distrust, or reliance between anthropomorphic and non-anthropomorphic conditions. Subgroup analyses accounting for domain expertise and general attitudes towards AI yielded similar null results. These results suggest that anthropomorphic descriptions alone are insufficient to shift trust-related concepts (trust, distrust, and reliance). Further, the results indicate boundaries for metaphorical framing effects in general and, specifically, for trust.

Many previous studies on the conceptual function of metaphors have focused on their function of highlighting aspects of target concepts. From the beginning of this research, it was known that conceptual metaphors also hide aspects of the target concept; however, this aspect has been less studied. This study builds upon the idea that the hiding aspect of a specific metaphor should be identified in relation to other metaphors for the same concept. A method is presented to detail this relation based on the theory of semantic frames and the FrameNet resource to identify the hidden aspects and apply it to a corpus of 298 elicited metaphor texts on the target concept of understanding. The analysis revealed that certain conceptual aspects are consistently hidden by a majority of metaphors, pointing to patterns in conceptualization. Using this approach, six aspects frequently hidden by metaphors were identified: Sociality, Transfer, Ownership, Perception, Foundation and Duration.