Consequences of Artificial Intelligence for Urban Societies

Using Impact-Aware AI to Make Smart Cities Socially Equitable

Full Grant Project (2021 - 2025)

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. To this end, we develop an innovative methodology combining expertise from computer science and social science. Using agent-based models (ABM), we analyze the effects of AI-based decisions on societal macro variables of social inequality such as income disparity. The data input for these ABMs consists of both Open Government Data, digital traces, 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 groundworks for what we call “Impact-aware AI” in urban contexts. With CAIUS, we accompany two smart city applications planned by partners in the Rhine-Neckar Metropolitan Region: dynamic pricing of parking space and traffic law enforcement via Internet-of-Things sensors. Our results contribute to 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.

Planning Phase (2019-2020)

“Smart Cities” is the buzzword for the development of data-driven processes in the public and governmental space to improve life quality, enable new applications or simply to optimise processes for more efficiency. Artificial intelligence plays a key role in the implementation of smart city services, for example in detecting patterns in the usage of public services to optimize the service quality, or in the identification of unusual behaviour or measurements to trigger an alarm. The basis for such smart services is always data, which can be generated specifically for these services (such as the installation of air pollution sensors) or which can be made available from existing government data or other data sources which are relevant for the general public (e.g. traffic information, public transportation). This project focuses on a potential negative effect of smart cities with the question: Where do smart city applications lead to potential erosion of solidarity of the urban society?

Erosion of solidarity (or desolidarisation) in the context of big data / data mining applications is a well-known, yet not a well researched problem, where profiling and high personalization leads to disadvantages for people with a bad profile, or with no profile at all. Similar processes also exist in smart city applications. This notion of desolidarisation processes in the context of artificial intelligence applications, with a main focus on the urban society, is the central theme for this project.