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Putting humans back in the loop of machine learning in Canadian smart cities

Even as researchers recognize smart cities as sociotechnological assemblages, the attractiveness of artificial intelligence and machine learning (AI/ML) continues to drive cities towards automating urban process and analysis. Rather than arguing whether researchers should automate their analysis, we are interested in identifying where non‐automated, manual judgement calls are made and the analysis is more subjective than presented. We examine topic modelling, an ML method, in an analysis of applications to a pan‐Canadian smart cities grant competition. We document 11 steps in topic modelling from data collection to interpretation. At each step, including the choice of the topic modelling method, some degree of human intervention is required. We draw on human‐centred ML research to argue for a greater recognition of the role of humans‐as‐researchers to preclude further uncritical adoption of AI/ML to research smart cities.