Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
Samantha Chan, Pat Pataranutaporn, Aditya Suri, Wazeer Zulfikar,, Pattie Maes, Elizabeth F. Loftus

TL;DR
This study demonstrates that large language model-powered chatbots significantly increase false memory formation in simulated witness interviews, raising ethical concerns about AI use in sensitive investigative contexts.
Contribution
It reveals the specific impact of generative AI chatbots on false memory induction and identifies moderating factors influencing susceptibility.
Findings
Generative chatbots induce over 3 times more immediate false memories than control.
False memories from generative chatbots remain stable after one week.
Higher confidence persists in false memories induced by generative chatbots.
Abstract
This study examines the impact of AI on human false memories -- recollections of events that did not occur or deviate from actual occurrences. It explores false memory induction through suggestive questioning in Human-AI interactions, simulating crime witness interviews. Four conditions were tested: control, survey-based, pre-scripted chatbot, and generative chatbot using a large language model (LLM). Participants (N=200) watched a crime video, then interacted with their assigned AI interviewer or survey, answering questions including five misleading ones. False memories were assessed immediately and after one week. Results show the generative chatbot condition significantly increased false memory formation, inducing over 3 times more immediate false memories than the control and 1.7 times more than the survey method. 36.4% of users' responses to the generative chatbot were misled…
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Taxonomy
TopicsInterpreting and Communication in Healthcare · Topic Modeling · Deception detection and forensic psychology
