When
Where
AI Driven Nudges: Self-driving cars, Energy consumption, and whatnot…
Abstract: Nudges are known to alter human behavior, often invoking persuasion. In particular, AI-driven nudges could be more effective given their tailored nature and adaptability. This talk will introduce nudges and discuss their effectiveness with a particular focus on AI-driven nudges in two specific contexts, self-driving cars and the consumer energy domain.
The first study will demonstrate the application of nudges to understand the impact on trust in self-driving cars or automated driving systems. Automated driving systems provide a means of reducing the inherent danger of operating a personal motor vehicle. However, barriers to adoption exist due to low trust in the artificial intelligence that powers the systems. To fill this trust deficit, this work proposes a deep learning-based visual alert system that allows passengers to monitor the artificial intelligence performance in real time. Using a trained object detection model, we design a novel perception-augmentation system that conveys information about the driving scene to the passenger through the lens of artificial intelligence. We conduct an empirical study that confirms that the proposed system improves trust in the underlying artificial intelligence technology. Trust in artificial intelligence is also found to not only positively affect perceived benefits and intention to use an automated driving system but also negatively influence the perceived risk associated with using the technology. Perceived enjoyment from the autonomous vehicle is also found to have a strong effect on the perceived benefit and intention to use the system.
The second study will focus on understanding the impact of a particular type of AI-driven nudges in the consumer energy domain. In particular, we focus on fear/threat appeal to understand trust in AI-driven recommendations on consumers’ energy footprint. The further adoption of artificial intelligence (AI) continues to grow and put further strain on existing electricity infrastructure. While AI is a major driver for increased electricity demand, it offers a solution by allowing consumers to better understand and manage their electricity consumption. Protection Motivation Theory (PMT) has been used by researchers to investigate threat appeals in contexts ranging from healthcare to information security compliance. Traditionally, PMT has examined how threat appeals influence behavior intention through fear; we extend PMT by adding an alternate path for internal motivation via personal norms, as we posit that consumers will update their ethics in response to threat appeals, given that electricity consumption affects the environment and the consumer’s community. A panel of US residential consumers was formed, and we measured consumer sentiment toward AI-driven mobile applications that help users manage their electricity consumption. Consumers were presented with a scenario prompt for either a high or low threat.
Keywords: Automated Driving Systems, Autonomous Vehicle Adoption, Trust, Artificial Intelligence, Nudges, Alerts, Electricity, Fear Appeals, Mobile Applications, Protection Motivation Theory
Bio: Ashish Gupta is a Globe Life Professor of Analytics and PhD program Coordinator within the Department of Business Analytics & Information Systems at Raymond J. Harbert College of Business at Auburn University. His research interests are artificial intelligence, machine learning, natural language processing, healthcare informatics, IoT, sports analytics, and organizational and individual performance. His recent articles have appeared in journals such as MIT Sloan Management Review, European Journal of Information Systems, Decision Sciences Journal, European Journal of Operations Research, Information & Management, Risk Analysis, and the Journal of the American Medical Informatics Association. He has published five edited research books. Professor Gupta’s research has been supported by various grant agencies and several private organizations and has received numerous awards. His work has been supported by various funding agencies, including the DOD, DHS, NHTSA, and THEC. His efforts have led to grants totaling more than $4 million. He serves as Co-Editor-in-Chief of the Decision Sciences Journal of Innovative Education and as an editorial board member of Decision Support Systems, Journal of Business Analytics, and Information Systems Frontiers. He served as Program co-chair for AMCIS 2025 in Montreal, Canada. He is a Cum Laude Distinguished AIS member and has been recognized with the AMCIS Service Leadership Award and the AIS Team Leadership Award. He has served as the President of the MWAIS Chapter and SIGDSA, and as the chair of the SIGDSA advisory board. He has served on the organizing committees of ICIS and AMCIS, served as sponsorship chair for AMCIS 2026 and ICIS 2028, and has been a mentor for the ICIS doctoral student corner, ICIS mid-career faculty, AMCIS doctoral consortium, and AMCIS junior faculty consortium. He is the conference chair for AMCIS 2028 in Boston and ICIS 2029 in Sydney.