Measuring the Impact of AI and Machine Learning Research

Simon Rodriguez on building tools and metrics that help inform and shape public policy.

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In this episode of the Data Exchange, our special correspondent and managing editor Jenn Webb organized a mini-panel composed of myself and Simon Rodriguez,  Data Research Assistant at the Center for Security and Emerging Technology (CSET) at Georgetown University.  Through a series of reports and data briefs, CSET provides policymakers with data rich material to inform and guide public policy.

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Simon has been a co-author of several including two studies on the AI and machine learning research ecosystem viewed through publications and patents:

One of the topics we discussed is how research breaks out from the research community and into public consciousness. Simon and his collaborators have examined this question closely:

    ❛ What informs more about public consciousness when it comes to a paper? Is it peer citation activity? We know peer citation can be very bias to what is relevant to an academic field. But as to what’s relevant to people in a general sense, would Altmetric citation be more important?

    There are these major technology companies that are dominant in the major machine learning and AI conferences. But when we see how that affects the public consciousness, how many people are tweeting about it, how many people are talking about it on YouTube, those papers yield substantially less than more niche papers.

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