Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking

TL;DR

This study investigates how evidence and natural language explanations influence non-expert users' evaluation of AI-generated claims in fact-checking, highlighting evidence's crucial role in assessing AI reliability.

Contribution

It systematically examines the impact of evidence and explanations on users' decision-making in AI-supported fact-checking, revealing evidence's central role and interaction with explanations.

Findings

Participants relied on evidence to validate AI claims.

Natural language explanations reduced evidence use unless flawed.

Evidence helps users assess AI reliability.

Abstract

Although much research has focused on AI explanations to support decisions in complex information-seeking tasks such as fact-checking, the role of evidence is surprisingly under-researched. In our study, we systematically varied explanation type, AI prediction certainty, and correctness of AI system advice for non-expert participants, who evaluated the veracity of claims and AI system predictions. Participants were provided the option of easily inspecting the underlying evidence. We found that participants consistently relied on evidence to validate AI claims across all experimental conditions. When participants were presented with natural language explanations, evidence was used less frequently although they relied on it when these explanations seemed insufficient or flawed. Qualitative data suggests that participants attempted to infer evidence source reliability, despite source identities being deliberately omitted. Our results demonstrate that evidence is a key ingredient in how people evaluate the reliability of information presented by an AI system and, in combination with natural language explanations, offers valuable support for decision-making. Further research is urgently needed to understand how evidence ought to be presented and how people engage with it in practice.