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DTSTART:20070311T020000
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DTSTAMP:20241224T110613
DTSTART;TZID=America/Detroit:20250107T160000
DTEND;TZID=America/Detroit:20250107T170000
SUMMARY:Meeting:CSAS Summer in South Asia Information Session
DESCRIPTION:Attend via Zoom: https://myumi.ch/nynnm\n   \n   Meet our SiSA mentors and learn about the Center for South Asian Studies' Summer in South Asia Fellowship. Don't miss this chance for undergrads to have a fully funded\, life-changing experience in India this summer.\n   \n   https://ii.umich.edu/csas/undergraduate-students/summer-in-south-asia-fellowships.html\n   \n   The application closes on January 15.\n\nIf there is anything we can do to make this event accessible to you\, please contact us. Please be aware that advance notice is necessary as some accommodations may require more time for the university to arrange.
UID:130330-21865762@events.umich.edu
URL:https://events.umich.edu/event/130330
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Fellowships,internships,India,Funding,Asia
LOCATION:Off Campus Location
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20241223T141301
DTSTART;TZID=America/Detroit:20250107T160000
DTEND;TZID=America/Detroit:20250107T170000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Josh Loftus\, Assistant Professor\, Statistics and Data Science\, London School of Economics
DESCRIPTION:Abstract: Tools for interpretable machine learning or explainable artificial intelligence can be used to audit algorithms for fairness or other desired properties. In a \"black-box\" setting--one without access to the algorithm's internal structure--an auditor can only use model-agnostic methods based on varying inputs while observing differences in outputs. These include popular interpretability tools like Shapley values and Partial Dependence Plots. But such methods have important limitations that can impact audits with consequences for outcomes such as fairness. In high-stakes applications\, it may be worth the effort to use tools that can incorporate background information and be tailored for specific use-cases. We introduce promising ways to do this using the mathematics of causality\, with Causal Dependence Plots serving as an example. Causal interpretability illustrates a broader research agenda of a more human-centered data science\, empowering people with tools to consciously guide the directions of research and technology for our purposes.\n\nhttps://joshualoftus.com/
UID:130085-21865299@events.umich.edu
URL:https://events.umich.edu/event/130085
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 411
CONTACT:
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