Hi all,
Our next virtual meeting will be Wednesday, September 30, where we will
hear Michael Baiocchi (Stanford University) presents research on "When
black box algorithms are (not) appropriate: a principled prediction-problem
ontology."
Abstract:
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.
Zoom link:
https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09
(Login
required)
When: Wednesday, September 30 at 12noon -- 1:30pm.
The information and schedule of the seminar can be found on our website
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home> and
Google calendar
https://bit.ly/30QZJ9k.
Best,
Soichiro Yamauchi
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/