Trivik Verma on "Just Data"

The primary purpose of this session is to teach future data scientists to look beyond the technical power of artificial intelligence and recognise the possibilities and limitations of data and the spatial inequalities that galvanise as a result of data-driven technology and policy. This session will engage students at the intersection of data science, urbanisation, and effective communication. By interrogating the sociotechnical nature of urban problems, students should then be able to approach solutions to these problems in ways that prioritise social equity and justice. In the last decade, technological advancements have led us to embed large-scale networked systems, sensors, and computers into the built environment. Urban data has emerged as an excellent stream of constant, real-time, and accurate information about all urban activities. The big data revolution, coupled with the capacity of infrastructure to be “smart” has enticed cities and urban managers worldwide to participate in machine learning-based decision-making for improving the course of humanity. But city planning has largely been instituted around loosely coupled organisations within municipal and regional governments, project developers, companies and investors, transport, water, and energy operators. While some communities have enjoyed the benefits of policies based on the use of big data, machine learning and AI, many have also suffered disproportionately by being pushed to the physical and technological periphery of rapid development in cities. As data scientists, and especially as engineering and policy analysts, it is our responsibility to interrogate the quality of data, the design of intelligent systems and their impact on communities.

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