Beyond Tech: Machine learning in science & policy

We are the UK’s national institute for data science and artificial intelligence.The Institute is named in honour of Alan Turing, whose pioneering work in theoretical and applied mathematics, engineering and computing is considered to have laid the foundations for modern-day data science and artificial intelligence. The Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, driving economic impact and societal good, lead the training of a new generation of scientists, and shape the public conversation around data.Click here to join our newsletter

The Alan Turing Institute

Seminar with David Dunson, Arts and Sciences Distinguished Professor, Departments of Statistical Science, Mathematics, and Electrical & Computer Engineering, Duke University
Beyond Tech: Machine learning in science & policy 
Applications in the tech industry have driven much of the progress in machine learning in recent years.  Most tech applications are fundamentally different in structure than applications in other fields, such as science and policy making.  Hence, algorithms that can be highly successful in tech can be dramatically unsuccessful in other domains.  In this talk, I will provide a brief review of the types of application areas in which widely popular machine learning algorithms (e.g., deep learning) perform well and will highlight key differences between these settings & other areas.  I will argue that there can be disastrous results in naively applying off-the-shelf ML algorithms in areas including criminal justice (e.g, automating sentencing and bail), science (e.g, neuroscience), policy (e.g., regulating chemical exposures), and health decision making.  Instead, one needs to carefully develop targeted methods that deal with crucial issues of selection bias, uncertainty quantification, limited training data, and complex/high-dimensional observations.  As illustration, I focus in more detail on two problems: (1) removing the influence of a sensitive variable (e.g, race/ethnicity) to obtain a fair predictive algorithm (also related to causal inference and privacy); and (2) obtaining interpretable predictive models of human traits based on an individual’s brain connection structure.  
Sponsored by the Lloyd’s Register Foundation Programme on Data-Centric Engineering.
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Alan Turing Institute

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Beyond Tech: Machine learning in science & policy

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Beyond Tech: Machine learning in science & policy

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