Nghi Truong | Search arXiv

arXiv2026

Why They Disagree: Decoding Differences in Opinions about AI Risk

Identifying the reasons for disagreements between influential points of view on issues that affect the public can help produce informed policy responses, even if they do not bring disagreeing parties closer to agreement. We present a methodology for extracting reasoning chains - the sequences of premises that motivate or justify opinions - from natural discourse, and for characterizing the types of premises (facts, forecasts, definitions, causal beliefs, and evaluations) that make up these chains. We demonstrate the utility of this approach for two practical goals: diagnosing specific points of contention and aggregating arguments across speakers. We illustrate the methodology through an analysis of the debates on the nature of risks that AI poses to the public, using a corpus of interviews from the Lex Fridman podcast. We find that differences in perspectives among the podcast's guests on existential risk and employment risk from AI arise primarily from differences in causal premises and forecasts, whereas in the case of AI's effects on human social relationships, premises regarding what is valued and definitions about what counts as genuine human connection play a distinctively larger role. Our approach to analyzing reasoning chains at scale, using an ensemble of LLMs to parse textual data, can be applied to facilitate deliberation and aggregation of opinions on any topic.

cs.CY↗

arXiv2025

Why Trust in AI May Be Inevitable

In human-AI interactions, explanation is widely seen as necessary for enabling trust in AI systems. We argue that trust, however, may be a pre-requisite because explanation is sometimes impossible. We derive this result from a formalization of explanation as a search process through knowledge networks, where explainers must find paths between shared concepts and the concept to be explained, within finite time. Our model reveals that explanation can fail even under theoretically ideal conditions - when actors are rational, honest, motivated, can communicate perfectly, and possess overlapping knowledge. This is because successful explanation requires not just the existence of shared knowledge but also finding the connection path within time constraints, and it can therefore be rational to cease attempts at explanation before the shared knowledge is discovered. This result has important implications for human-AI interaction: as AI systems, particularly Large Language Models, become more sophisticated and able to generate superficially compelling but spurious explanations, humans may default to trust rather than demand genuine explanations. This creates risks of both misplaced trust and imperfect knowledge integration.

cs.AI↗