An up-to-date list of prior publications can be found on Google Scholar

Philosophy of Science and Artificial Intelligence

My theoretical work in the philosophy of artificial intelligence is informed by both the philosophy of science and a rigorous, technical engagement with the contemporary AI research literature. In my philosophical work, I analyze how these tools can be understood to give us knowledge and how these tools are situated within the methodological landscape of science. My current projects focus on the nature of distributed representations and how these can and cannot serve as scientific representations. In the ethics of AI, I focus on bias, but also on questions concerned with the need for disciplinary integration in AI systems development.

Manuscripts in Review
"Deep Learning and Scientific Models"

Manuscripts in Preparation
"Neural Networks and the Aims of Science"
"The Rationalist Ghost in the Machine"

Computational Social Science

My empirical work in the computational social sciences is concerned with the dynamics of discovery across disciplines, geographic space, and time. My approach applies contemporary AI methods for NLP (vector space embedding) and prediction (deep neural networks) as well as computational, statistical analysis to large scale observational data and field experiments. I regularly collaborate with applied and basic scientists, social scientists, humanists, as well as industry professionals. My publications have been featured on CNN, in Scientific American, MIT Technology Review, Harvard Business Review, Forbes, and elsewhere.

Manuscripts in Review
Eamon Duede, Misha Teplitskiy, Karim R. Lakhani, and James A. Evans. "Being Together in Place as a Catalyst for Scientific Advance"
Misha Teplitskiy, Eamon Duede, Michael Menietti, and Karim R. Lakhani. “Status drives how we cite: Evidence from thousands of authors"

Manuscripts in Preparation
Eamon Duede and James A. Evans. "The Social Abduction of Science"

Recent Research Grants and Awards

Latent Knowledge Capture on Covid-19 Literature
As the published (and preprint) literature on Covid-19 explodes, no individual researcher, team, or institution can cognitively process and understand what we now collectively know about this disease. Many connections and relationships between and across properties of the virus, its interaction with hosts, its transmission within populations, as well as health and economic outcomes are missed precisely because the expertise needed to make these connections spans otherwise disparate and cognitively disjoint disciplines (microbiology, virology, epidemiology, medicine, and public policy). As a result, we may know more about this disease than we are collectively aware. As the number of these “unknown knowns” piles up, we are missing opportunities for breakthroughs. This suggests the need for computational approaches to reveal the correlations latent in our collective understanding. However, because that understanding is codified as text (as opposed to, say, structured data), it lacks the kind of statistical tractability necessary for traditional machine learning approaches to modeling. Here I propose to develop novel unsupervised word embedding models to capture and extract knowledge about Covid-19 that is otherwise latent in and across the blooming literature.

Personnel: Eamon Duede (UChicago).
Funding provided by: Google

When Technology Transforms Society: Considering the Societal and Ethical Impacts of Quantum Computing and AI Quantum computing and artificial intelligence are currently making significant technical progress, with commensurate interest from the public, media outlets, funding agencies, and corporate partners. Stakeholders frequently point to the potential of these technologies to “transform society,” but what does this mean, practically? Should we, as researchers, anticipate the social, political, and ethical consequences of our work and steer our research programs accordingly? Can we draw from scholarship in the social sciences and the humanities to inform understanding of the distributional impacts of our programs? This workshop will explore these questions and develop collaborations across disciplines, institutions, and key stakeholders who may be able to help responsibly steer the evolution of these revolutionary technologies in ethical and socially beneficial ways.

Personnel: Daniel Bowring (Fermilab), Chihway Chang (UChicago), Eamon Duede (UChicago), and Brian Nord (Fermilab). Funding provided by: Center for Data and Computing at the University of Chicago and the Kavli Foundation