SOCIAL SUPPORT FOR THE REAL-WORLD INTRODUCTION OF AI IN CRITICAL INFRASTRUCTURE
The social impact of technology on its users has been vastly proved to be enormous (King and He, 2006) since it might pose organizational and social obstacles. Acceptance of new technologies, especially in the energy field, has been recognized as one of the primary barriers to implementing technological innovations (Huijts, Molin, and Steg, 2012). In the context of the ILUSTRE project, the impact of IT technology on the energy and water supply domains is twofold. First: the impact on the partners industry managers and employees. The implementation creates disruption, and unless managers support the innovation and workers understand and comply with the new infrastructure, they might actively oppose the implementation. Second: the impact on the broader society since AI technology will affect the quality and the features of the services provided to the population. The scientific challenge addressed in this Ph.D.-project is to employ group model building (GMB) and social network analysis (SNA) to monitor the extent to which the employees and the larger public (together called the stakeholders) receive and respond to the implementation. GMB is a widely used approach to collect data and monitor (and influence) the opinions and sentiments of groups of stakeholders (Peck, 1998). SNA has been used extensively to analyze the group dynamics at the roots of technological implementation reception (Sasovova and Leenders, 2009). These techniques can well be used in conjunction with agent-based simulation models.
PhD promoters: Dr. Claudia Zucca and prof. Dr. Roger Leenders