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Grant Fleming | Fairness and Data Science: Failures, Factors, and Futures | RStudio

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.

About Grant: Grant Fleming 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

rstudio tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Ethics Propublica Tech

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Grant Fleming | Fairness and Data Science: Failures, Factors, and Futures | RStudio