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X-WR-CALNAME;VALUE=TEXT:Workshop in Applied Statistics
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SUMMARY:Workshop in Applied Statistics
DESCRIPTION:<p>	You can attend this workshop via this link: https://harvard.zoom.us/j/99424949004</p><p>	If you would like to be added to the email list to receive reminders and information about the series, please send your email address to Soichiro Yamauchi (syamauchi@g.harvard.edu).</p><h2>	Today's presenter</h2><p>	Michael Baiocchi (Stanford University), "When black box algorithms are (not) appropriate: a principled prediction-problem ontology"</p><!--break--><h2>	Abstract</h2><p>	In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. Though this type of reasoning has come to dominate areas of data science, it has been under-discussed and its impact under-appreciated. For example, it is the primary way we reason about "black box'' algorithms. In this talk we discuss its current use (i.e., as "the common task framework'') and its limitations; we find a large class of prediction-problems are inappropriate for this type of reasoning. Further, we find the common task framework does not provide a foundation for the deployment of an algorithm in a real world situation. Building off of its core features, we identify a class of problems where this new form of reasoning can be used in deployment. We purposefully develop a novel framework so both technical and non-technical people can discuss and identify key features of their prediction problem and whether or not it is suitable for this new kind of reasoning.</p><p>	<em>The Applied Statistics Workshop (Gov 3009) is a forum for graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. It is co-hosted by Harvard University's Department of Government and Institute for Quantitative Social Science (IQSS).</em></p><p>	For more information, visit the <a data-url="https://projects.iq.harvard.edu/applied.stats.workshop-gov3009" href="https://projects.iq.harvard.edu/applied.stats.workshop-gov3009" title="">Applied Statistics website</a>.</p>
LOCATION:Zoom - see below
STATUS:CONFIRMED
DTSTART:20200930T160000Z
DTEND:20200930T173000Z
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