Algorithmic Bias in City Scale Digital Twin Data Management Processes: A qualitative exploration of the pragmatic, ethical, and moral prioritization surrounding algorithmic bias in city scale digital twin projects

Abstract: The application of digital twins in the city setting is emerging, the massive amounts of data and algorithms used raise the concern of algorithmic bias. This research focuses on examining how city scale digital twin practitioners prioritize the pragmatic, ethical, and moral domains in data management processes and when encountering algorithmic bias. It aims to provide insights for practitioners and urban planners to navigate these complex challenges while considering potential trade-offs between domains. A qualitative research approach was chosen to understand decisions in data management processes during the planning and design of city scale digital twins, with semi-structured interviews as the primary data collection method. This study found that in data management processes and when encountering algorithmic bias in city scale digital twins, the pragmatic domain was consistently prioritized, while the ethical and moral domains received less emphasis. Trade-offs between the pragmatic and moral domains were more prevalent than those between the pragmatic and ethical domains. The findings highlight the need for an ethical code of conduct and common standards in data handling to uphold moral values, and the importance of involving all stakeholders in the discourse, including citizens.