Grant Fleming to Speak at the RStudio Global 2021 Conference
Event Date:
January 21, 2021 12:00 am

Grant Fleming to Speak at the RStudio Global 2021 Conference

Grant Fleming will deliver a talk on “Fairness and Data Science: Failures, Factors, and Futures” at the virtual RStudio::Global 2021 Conference, a free event, on January 21 & 22, 2021.

Session Abstract

In recent years, numerous highly publicized failures in data science have made evident that biases or issues of fairness in training data can sneak into, and be magnified by, our models, leading to harmful, incorrect predictions being made once the models are deployed into the real world. But what actually constitutes an unfiar or biased model, and how can we diagnose and address these issues within our own work? In this talk, I will present a framework for better understanding how issues of fairness overlap with data science as well as how we can improve our modeling pipelines to make them more interpretable, reproducible, and fair to the groups that they are intended to serve. We will explore this new framework together through an analysis of ProPublica’s COMPAS recidivism dataset using the tidymodels, drake, and iml packages.

Interested in Attending?

Grant will be presenting at two times:

  • Thursday, Jan 21, 2021 at 6:00 PM to 6:23 PM EST
  • Friday, Jan 22, 2021 at 6:00 AM to 6:23 AM EST
Register for the event

About the RStudio::Global 2021 Conference

The free 24-hour virtual event is designed around participation from every time zone and will focus on all things R and RStudio. The conference includes 50+ speakers from around the world, 3 keynotes, 30 talks with live Q&A, 30 rapid fire lightning talks, social events, and more.  This is the premiere yearly conference for those interested in R.

About Grant Fleming

Grant is a Data Scientist at Elder Research, co-author of the Wiley book Responsible Data Science (2021), and contributor to the O’Reilly book 97 Things About Ethics Everyone in Data Science Should Know. His professional focus is on machine learning for social science applications, model explainability, and building tools for reproducible data science. Previously, Grant was a research contractor for USAID.