Section outline

  • Lecture 12: Case Studies

    Faculty:  Aaron Wolfe Scheffler

    We will explore some case studies to practice our skills in drawing up a machine learning analysis plan, much as you would for your own future projects. Some materials discussing ethics in Big Data are linked below for those curious. 

    Ethics in Big Data

    Ethical application of machine learning to big data is an important topic and I would like to provide some resources for you to archive and peruse at your own leisure. As clinical researchers, the approaches and considerations we take when analyzing data have implications on treatment and health outcomes. Even the most well intended applications of machine learning to big data can have unanticipated consequences that produce bias or even discrimination. Therefore, please keep these topics in mind as you develop your future research projects.

    Resources:

    Online courses: 
    • Practical data ethics: an online short course on data ethics by https://ethics.fast.ai/. Very organized structure with recorded video lectures.
    Books:
    • Cathy O'Neil. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishers, 2016.
    • Broussard, M. (2018). Artificial Unintelligence: How Computers Misunderstand the World. MIT Press.

    Journal articles with discussion of bias in machine learning with clinical applications:

    • Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med. 2020;383(9):874-882. doi:10.1056/NEJMms2004740
    • Ziad Obermeyer and Sendhil Mullainathan. 2019. Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Association for Computing Machinery, New York, NY, USA, 89. DOI:https://ucsf.idm.oclc.org/login?url=https://doi.org/10.1145/3287560.3287593

    • Large Group Discussion (Access restricted to registered students): Brief formal review of lecture followed by question and answer discussion. Recorded lecture should be viewed prior to this session.

    • Biostat 202 Lecture 12 - RECORDED Zoom URL
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