This study explores how labeling automotive AI as either "trustworthy" or "reliable" influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research examines various dependent variables related to the Technology Acceptance Model (TAM), including perceived ease of use, perceived usefulness, attitude, behavioral intention, and human-like and functionality-based trust. By analyzing responses from participants assigned to different AI labels, the study aims to provide insights into how specific labels affect attitudes toward using, learning to use, technology acceptance, and adopting certain attitudes toward automotive AI technology. The findings will contribute to understanding the role of labeling in shaping user attitudes and trust towards AI systems.

Link to the Study's OSF Homepage

This study is being conducted by John Dorsch (LMU-Munich) and Ophelia Deroy (LMU-Munich).

For this study, we trained a Large Language Model (GPT-3, like ChatGPT) on the philosopher Dan Dennett's work and posed it 5 philosophical questions, which we also asked Dan to answer. For each philosophical question, there is one answer by Dan and 4 by the LLM. Your task is to try to determine which one was actually written by Dan. We also ask you to describe how you tried to determine which one was written by an actual philosopher.

Link to study

This study is being conducted by Anna Strasser (DenkWerkstatt Berlin, LMU-Munich), John Dorsch (LMU-Munich), and David Schwitzgebel (Institut Jean Nicod, École Normale Supérieure-PSL).