Xinyi Yuan

Interests

urban analysis, machine learning, Explainable Artificial Intelligence (XAI), spatial analysis tools

Bio

I am a PhD student in Geospatial Data Science within the team at the University of Glasgow, having joined in Fall 2022. Prior to this, I earned my MEng in Urban Planning from Southeast University (China) and my BEng in Urban Planning from Chongqing University (China). My current research focuses on the application of Explainable Artificial Intelligence (XAI) in cities and society. I’m also interested in development and application of spatial analysis tools.

Research

Xinyi Yuan is a PhD candidate in Geospatial Data Science. Her research focuses on the application of Explainable AI (XAI) in urban studies. Prior to her PhD studies, Xinyi obtained a Master’s degree in Urban Planning. Her current research leverages large-scale building footprint data and Explainable AI to explore the relationship between urban development and socioeconomic characteristics in England.

Although machine learning is effective in revealing critical and hidden relationship within cities, the ‘black box’ nature of AI-based systems used in urban management and the policy-making process could cause a crisis of confidence in model results among researchers, governments and the public. Explainable AI, aiming at providing interpretability for machine learning models, is a promising way to address this issue. By building trust in machine learning models and justifying their predictions through Explainable AI, it enables stakeholders to make more informed, transparent, and accountable decisions for the urban future.