- Geo-political economy of AI (e.g., urban-rural/north-south divides, concentrations of AI resources that effectively create AI citystates, location of IP and patents, trade agreements)
- Feature detection of space and the classification of place. Applications of location (e.g., zip codes, space-time trajectories) and placenames in AI/ML to discriminate against people and places
- AI and structural racism (Benjamin 2019; Noble 2018)
- Critiques of innovation and disruption and alternatives of slow infrastructures (Barlow & Drew 2020), slow computing (Kitchin and Fraser 2020) and slow AI (Crampton 2020)
- Role of civil society to participate in automated decision making (including the ability to counter AI)
- Intersection between Critical Data Studies and Critical GeoAI, for example the role of data in AI, its collection and curation; geographical source of the training data
- Role of optimization and performance metrics; the geography of standards setting and ethics and trust frameworks
- “Local AI” and, drawing on Castells (2010), the spatial logic of AI “flows”. How AI “travels” between sources of origin and places of application
- Algorithmic colonization, extractivism and Western complicity (Birhane 2020)
- Scalar nature of AI (e.g., digital infrastructures like data centers)
- The physicality of AI via its environmental impacts, including calls for a sustainable AI
- Carceral and decarceral AI
Ultimately, we seek to address what is special about spatial in the critique of AI. How does a geographic lens differ from a legal, computational, communications or political science lens?
THIS IS A VIRTUAL SESSION. Please send your abstracts of 250 words or less to email@example.com and firstname.lastname@example.org by October 20.
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