Psychology of AI
Psychology of AI
Data science by people for people
Successful introduction of advanced analytics—whether in companies or market offerings—ultimately rests on human users’ beliefs and adoption behavior. The Psychology of AI lab examines the human side of data science. We study a variety of topics, including consumer acceptance of AI solutions and of automated products, workers’ beliefs about technological replacement of human labor, and how analysts make sense of data.
From a business perspective, data science projects and AI-driven innovations can only be successful when they are positively received and correctly deployed by managers and customers. Important psychological processes such as social comparison, attribution, need for uniqueness, and self-consciousness explain how individuals react to, and think about, intelligent machines. We conduct experiments with human participants and apply a behavioral science approach to AI.
From a societal perspective, current forecasts predict the displacement of significant sections of the labor force, due to the increasing ability of robots and algorithms to automate tasks. Regardless of whether this displacement will be transitional or permanent, it is important for the stability of society to understand potential threats to the psychological wellbeing of affected individuals. Our lab investigates the psychological consequences of technological replacement of human activities.
Autonomy in consumer choice
Consumers and Artificial Intelligence: An Experiential Perspective
Linear Thinking in a Nonlinear World
Man Versus Machine: Resisting Automation in Identity-Based Consumer Behavior
Preference for Human (vs. Robotic) Labor is Stronger in Symbolic Consumption Contexts
Psychological Reactions to Human Versus Robotic Job Replacement
- Stefano Puntoni, Professor and Director // Adoption of automation, AI experiences, technological unemployment, decision-making with data
- Daan Stam, Professor of Leadership for Innovation // Leadership, innovation and AI
- Anne Klesse, Associate Professor // Tech devices and decision-making, algorithmic recommendations
- Gabriele Paolacci, Associate Professor // Crowdsourcing, ethics of AI
- Mirjam Tuk, Associate Professor // Self-control, perceptions of technology and educational choices
- Johannes Boegershausen, Assistant Professor // Acceptance of robots, technological unemployment
- Romain Cadario, Assistant Professor // Acceptance of medical AI
- Helge Klapper, Assistant Professor // Organization Design and AI
- Almira Abilova, PhD student // Perceptions of technology and educational choices
- Begum Celiktutan, PhD student // Tech devices and decision-making
- Mohamadreza Hoseinpour, PhD Student // Algorithmic Leadership, Algorithmic Management, Human-AI Interaction
- Gizem Yalcin, PhD student // Perceptions of algorithmic decision makers, algorithmic recommendations