Katie Dagon (Mar 29 2022 at 20:49): Katie Dagon (Mar 29 2022 at 20:49):The next ICBINB seminar series is on Thursday the 7th, April 2022 at 10am EDT / 4pm CET, with Cynthia Rudin as Invited Speaker (details below):
When: April 7th, 2022 at 10am EDT / 4pm CEST
Where: RSVP for the Zoom link here: https://us02web.zoom.us/meeting/register/tZ0kf-GpqTksH9QNO7jTWhfOYen1kLGkH_Rz
Title: Applications Really Matter (And Publishing Them Is Essential For AI & Data Science)
Abstract: Many of us want to work on real-world machine learning problems that matter. However, it’s really hard for us to focus on such problems because it is extremely difficult to publish applied machine learning papers in top venues. I will argue that the lack of respect for applied papers has several wide-ranging applications:
1) Benefits to Science: We are unable to leverage scientific lessons learned through applications if we cannot publish them. Applications should actually be driving ML methods development. It is important to point out that applied papers are scientific. A boring bake-off or technical report is not a scientific applied paper. An applied scientific paper provides knowledge that is systematized and generalizes, just like any good scientific paper in any area of science.
2) Benefits to the Real World: We publish overly complicated methods when simpler ones would suffice. If we could focus on solving problems rather than developing methods, this issue could vanish. Much more importantly, if we actually focus on problems that benefit humanity, we might actually solve them.
3) Broadening our Community: By limiting our top venues mainly to methodology papers, we limit our community to those who care primarily about methods development. This further limits our community to those who come from narrow training pipelines. It also limits our field to exclude those whose primary goal is to directly improve the world. A really good applied data scientist from any country should be able to publish in a top tier venue in data science or AI.
4) Freeing our Top Scientists: By tying promotions of our top data scientists to publication venues that accept (essentially only) methodology, it means our top scientists cannot focus on real-world problems. This is particularly problematic if one wants to publish a data science paper in an area for which a specialized journal does not exist.
My proposed fix is to have tracks in major ML conferences and journals that focus on applications.
Bio: Cynthia Rudin is a professor at Duke University. Her goal is to design predictive models that are understandable to humans. She applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from AAAI (the “Nobel Prize of AI”). She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and AAAI. She is a three-time winner of the INFORMS Innovative Applications in Analytics Award. Her work has been featured in news outlets including the NY Times, Washington Post, Wall Street Journal, and Boston Globe.
For more information and for ways to get involved, please visit us at http://icbinb.cc/, Tweet to us @ICBINBWorkhop, or email us at cant.believe.it.is.not.better@gmail.com.
Last updated: May 16 2025 at 17:14 UTC