The impact of ChatGPT on reliance on AI advice

• Autor(a) principal: Kaur, Roma
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/42662
• topic: Artificial Intelligence; AI advice; Trust in AI; Reliance on advice; Over-reliance on AI; Under-reliance on AI; Appropriate reliance; Inteligência artificial; Conselhos sobre IA; Confiança na IA; Confiança nos conselhos; Confiança excessiva na IA; Confiança insuficiente na IA; Confiança adequada; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

To improve decision-making, organizations have been increasingly employing AI tools. The use of AI in human decision-making gave rise to algorithm aversion or over-reliance on AI when users do not appropriately rely on AI advice. Literature names the lack of familiarity with AI as a reason for the inability of users to rely on AI advice appropriately. Since ChatGPT has gained global interest, there have been concerns about ChatGPT and other Large Language Models (LLMs) being misused. This thesis argues that familiarity with AI improves appropriate reliance on it. The thesis presents two studies that measure how experience with ChatGPT impacts trust in other AI tools, appropriate reliance, and detection of incorrect information. The results from these studies suggest that experience with ChatGPT improves appropriate reliance on it, which in turn improves decision-making, suggesting that ChatGPT and other AI tools can be used to increase users’ ability to distinguish between correct and incorrect AI advice and to act accordingly, by relying on it when it is correct. Furthermore, seeing ChatGPT err decreases trust in it and improves future verification of the information it provides, further improving appropriate reliance on AI. Nevertheless, it is still easier for individuals to determine when another individual erred than when ChatGPT did.