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  • Colloquium on Digital Transformation Science

  • September 24October 15, 3 pm CT

    Improving Fairness & Equity in Policy Applications of Machine Learning

    Rayid Ghani; Distinguished Career Professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy, Carnegie Mellon University

    COVIDScholar: Applying Natural Language Processing at Scale to Accelerate COVID-19 Research

    Gerbrand Ceder, Chancellor’s Professor, Department of Materials Science and Engineering, University of California, Berkeley

    Amalie Trewartha, Postdoctoral Scholar, Division of Materials Science, Lawrence Berkeley National Laboratory


    We are in the early stages of using AI, ML, and Data Science to help make better policy decisions. Governments and nonprofits have started to explore how to improve society by tackling problems such as preventing children from getting lead poisoning, reducing police violence and misconduct, and increasing vaccination rates. As the impact of the COVID-19 pandemic has continued to increase, both ML researchers and practitioners have been proposing and developing methods to better understand, predict, and mitigate the spread, and governments have been exploring the use of these tools to better guide policy decisions. The AI and ML tools will hopefully be important elements of society’s efforts to overcome COVID-19, but as with any application of machine learning and AI, they also pose risks of introducing or exacerbating disparities. In this talk, I’ll give examples of recent work that highlights the use of ML/AI to achieve fair and equitable outcomes and challenges that need to be tackled in order to have social and policy impact in a fair and equitable manner.

    Rayid Ghani is a Distinguished Career Professor in the Machine Learning Department and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. Rayid is a reformed computer scientist and wanna-be social scientist, focused on using large-scale Artificial Intelligence/Machine Learning/Data Science to solve public policy and social challenges in a fair and equitable manner. Rayid works with governments and non-profits on policy in health, criminal justice, education, public safety, economic development, and urban infrastructure. Rayid is passionate about teaching practical data science and started the Data Science for Social Good Fellowship that trains computer scientists, statisticians, and social scientists to work on data science problems with social impact. Before joining Carnegie Mellon University, Rayid was the Founding Director of the Center for Data Science & Public Policy, a Research Associate Professor in Computer Science, a Senior Fellow at the Harris School of Public Policy at the University of Chicago, and Chief Scientist of the Obama 2012 Election CampaignThere is a critical need for tools that can help the COVID-19 researchers stay on top of the emerging literature and identify critical connections between ideas and observations that could lead to effective vaccines and therapies for COVID-19. To this end, our team at UC Berkeley and Lawrence Berkeley National Laboratory is building, a knowledge portal tailored specifically for COVID-19 research that leverages natural language processing (NLP) techniques to synthesize the information spread across more than 140,000 emergent research articles, patents, and clinical trials into actionable insights and new knowledge. Having its origins in our text-processing work in Materials Science, COVIDScholar is powered by an automated system that scrapes research documents from dozens of sources across the internet, cleans/repairs metadata as necessary, and analyzes the text with a number of NLP models for classification, information extraction, and scientific language modeling. We then integrate this information with specialized knowledge graphs which has the potential to give users unparalleled insight into the complex interactions that govern the transmission of COVID-19, the disease’s progression, and potential therapeutic strategies. This approach to combining textual information, such as word embeddings, with ontological knowledge graphs has the potential to improve the performance of machine learning models that operate on these data structures and to enable new ways of exploring literature on emerging subjects by leveraging past knowledge more efficiently.

    Gerbrand Ceder is the Chancellor’s Professor of Materials Science and Engineering at the University of California, Berkeley. His research is in computational and experimental materials design for clean energy technology and in Materials Genome approaches to materials design and synthesis. He has published over 400 scientific papers and holds more than 20 U.S. and foreign patents. He is a member of the U.S. National Academy of Engineering and the Royal Flemish Academy of Belgium for Science and The Art, a Fellow of the Materials Research Society and the Minerals, Metals & Materials Society, and has received awards from the Electrochemical Society, the Materials Research Society, the Minerals, Metals & Materials Society, and the International Battery Association. He is Co-Lead Scientist for new battery technologies at the U.S. Department of Energy’s Joint Center for Energy Storage (JCESR) and Chief Scientist of the Energy Frontier Research Center at the National Renewable Energy Laboratory (NREL).

    Amelie Trewartha is a postdoctoral scholar in Gerbrand Ceder’s group at Lawrence Berkeley National Laboratory. She began her career as a nuclear physicist, before moving into materials science in 2019, with a focus on machine learning. Her research interests include the application of natural language processing (NLP) techniques to scientific literature, and building thermodynamically-motivated machine learning models for materials property prediction.

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