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AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.

AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery.
We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.
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We examine the prevalence and productivity dynamics of artificial intelligence (AI) in American manufacturing. Working with the Census Bureau to collect detailed large-scale data for 2017 and 2021, we focus on AI-related technologies with industrial applications.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.

While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
Using the same machine learning model for high-stakes decisions in many settings amplifies the strengths, weaknesses, biases, and idiosyncrasies of the original model. When the same person re-encounters the same model again and again, or models trained on the same dataset, she might be wrongly rejected again and again. Thus algorithmic monoculture could lead to consistent ill-treatment of individual people by homogenizing the decision outcomes they experience. This talk will formalize the measure of outcome homogenization, describe experiments on US census data that demonstrate that the sharing of training data consistently homogenizes outcomes, then present an ethical argument for why and in what circumstances outcome homogenization is wrong.
HAI Network Affiliate; Assistant Professor of Philosophy and Computer Science, Northeastern University
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