CAIUS: Consequences of AI for Urban Societies

Project Description

AI systems help to efficiently allocate scarce public resources and are at the core of many smart city activities. Yet, the same systems may also result in unintended societal consequences, particularly by reinforcing social inequalities. CAIUS will identify and analyze such consequences. Using agent-based models (ABM), the effects of AI-based decisions on societal macro variables of social inequality such as income disparity will be analyzed. The data input for these ABMs consists of both Open Government Data and own surveys. The goal is to train AI systems to account for their social consequences within specific fairness constraints; this synthesis of ABM and fair reinforcement learning lays the groundwork for what we call „impact-aware AI“ in urban contexts. Partnering with the Rhine-Neckar Metropolitan Region we investigate smart city applications and their impacts on the local populations. The results will contribute to the research of human-AI interaction and will be condensed into general guidelines for decision-makers regarding the ethical implementation of AI-based decision-making systems in urban contexts.


  • Kern C., Gerdon, F., Bach, R. L., Keusch, F. and Kreuter, F. (2022). Humans versus Machines: Who is Perceived to Decide Fairer? Experimental Evidence on Attitudes Toward Automated Decision-Making. Patterns.
  • Gerdon, F., Bach, R. L., Kern, C. and Kreuter, F. (2022). Social Impacts of Algorithmic Decision-Making: A Research Agenda for the Social Sciences. Big Data & Society.
  • Gerdon, F., Theil, C. K., Kern, C., Bach, R. L., Kreuter, F., Stuckenschmidt, H. und Eckert, K. (2020). Exploring impacts of artificial intelligence on urban societies with social simulations. 40. Kongress der Deutschen Gesellschaft für Soziologie, Online.