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Lauren Bennett

Publications and source records attributed to Lauren Bennett.

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Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography

Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similarly, in line with the nature of foundation models, our own research often relies on pre-trained models. Hence, understanding what world AI systems construct is as important as evaluating their accuracy, including factual recall. To motivate the need for such studies, we provide three illustrative vignettes, i.e., exploratory probes, in the hope that they will spark lively discussions and follow-up work: (1) Do models form strong defaults, and how brittle are model outputs to minute syntactic variations? (2) Can distributional shifts resurface from the composition of individually benign tasks, e.g., when using AI systems to create personas? (3) Do we overlook deeper questions of understanding when solely focusing on the ability of systems to recall facts such as geographic principles?

cs.AI

Measuring artificial intelligence: a systematic assessment and implications for governance

Governing artificial intelligence (AI) inventions is a major policy concern, yet definitions and measurement remain contested. We compare four patent-based approaches reflecting distinct understandings of AI. Using US patents (1990-2019), we assess the degree to which each approach classifies AI as a general-purpose technology (GPT) and examine patent concentration--two central policy-relevant dimensions. The approaches overlap in just 1.37% of patents, defining between 3-17% of all US patents in 2019 as AI. All approaches confirm AI's GPT characteristics, with the smallest keyword-based set exhibiting the highest growth and generality. High GPTness indicates public good characteristics, justifying public support. Across methods, AI patents concentrate among a few firms, highlighting market power and regulatory challenges. Policy implementation, thus, requires careful consideration of multiple classification methods to ensure robust, inclusive, and effective AI governance.

econ.GN