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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.

Recent Publications

Anne Kathrin Klesse

Dr. Anne Kathrin Klesse

Associate Professor of Marketing at RSM


  • Moniek Buijzen, Professor of Behavioural Change // Health and technology, peer influence
  • Steffen R. Giessner, Professor of Organizational Behavior and Change// AI and Change, AI and well-being
  • Wolf Ketter, Professor of Professor of Next Generation Information Systems // AI deployment, AI-human collaboration
  • René de Koster, Professor of Logistics and Operations Management // Human-machine collaboration, Internet-of-Things
  • Ting Li, Professor of Digital Business // AI-human collaboration, AI strategy, AI-enabled transformation
  • Daan Stam, Professor of Leadership for Innovation // Leadership, innovation and AI
  • Rebecca Hewett, Associate Professor // AI in people management, AI and wellbeing
  • Anne Klesse, Associate Professor // Tech devices and decision-making, algorithmic recommendations
  • Gabriele Paolacci, Associate Professor // Crowdsourcing, ethics of AI
  • Merieke Stevens, Associate Professor // Technology implementation, AI in manufacturing
  • 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
  • Alexander Genevsky, Assistant Professor // social credit systems, neuroscientific basis of behavior, market forecasting
  • Helge Klapper, Assistant Professor // Organization Design and AI
  • Antonia Krefeld-Schwalb, Assistant Professor // measuring preferences, temporal discounting, meta-science
  • Colin Lee, Assistant Professor // AI in recruitment and selection
  • Pieter Schoonees, Assistant Professor // Algorithmic bias, fairness, predictive modelling, model validation
  • Jelle de Vries, Assistant Professor // Human-machine collaboration in operations
  • Kamila Moulaï, Marie Curie Postdoctoral Researcher // The Influence of AI on Human Identity and Work
  • Bojan Simoski, Post-doctoral researcher // Machine learning, Health AI
  • Dijana Aleksic, Part-time PhD and HRIS Product Owner // Employee experience with AI, AI and well-being
  • 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
  • Tamara Thuis, PhD student // AI ethics, Explainable AI, Responsible AI
  • Gizem Yalcin, PhD student // Perceptions of algorithmic decision-makers, algorithmic recommendations
  • David Blok, Post-doctoral researcher // Systems thinking, agent-based modelling in health

Affiliated Experts

  • Emanuel de Bellis, Associate Professor // University of St. Gallen // Consumer perceptions of autonomous products and algorithms
  • Martin Paul Fritze, Assistant Professor // University of Cologne // Materiality, experiential marketing, technology & innovation diffusion
  • Jenny Lena Zimmerman, PhD student // University of St. Gallen // Consumer perceptions of, and evolving relationships with, autonomous products
  • Melanie Clegg, PhD student, University of Lucerne // Consumer perceptions of algorithms, AI, ethics of AI
  • Reto Hofstetter, Professor of Digital Marketing, University of Lucerne // Creativity and AI, Algorithm Perception, applying AI/ML in consumer research
  • Stefano Puntoni, Professor of Marketing and Director // Adoption of automation, AI experiences, technological unemployment, decision-making with data