Tuesday, July 13 from 9:00 AM - 1:00 PM CDT
Fairness in AI systems is an interdisciplinary field of research and practice that aims to understand and address some of the negative impacts of AI systems on society. In this tutorial, we will walk through the process of assessing and mitigating fairness-related harms in the context of the U.S. health care system. This tutorial will consist of a mix of instructional content and hands-on demonstrations using Jupyter notebooks. Participants will use the Fairlearn library to assess ML models for performance disparities across different racial groups and mitigate those disparities using a variety of algorithmic techniques.
During the tutorial, we will be using Google Meet for a 30-minute breakout session to discuss the material in small groups. Participation in the breakout session is encouraged, but not required to follow along with the tutorial.
For this tutorial, we encourage participants to run the tutorial notebook through Google Colab to avoid issues with local environment set-up. Click on the button above to launch the free compute environment for executing the Jupyter notebook and writing Python code.
If you want to follow along in this tutorial on your local machine, we recommend using the Anaconda Python distribution.
Participants will need to download the Jupyter notebook fairness-in-AI-systems-student.ipynb.
In a Python virtual environment (Python version >= 3.7), install the necessary libraries by running the following command:
pip install -r requirements.txt
If you are using pip20.3, you may need to append the --use-deprecated=legacy-resolver flag to avoid long wait times due to dependency resolution:
pip install -r requirements.txt --use-deprecated=legacy-resolver
You can run the checkenv.py script to assert if the packages were installed correctly.
For this tutorial, we use a pre-processed version of the U.S. Hospital Diabetes dataset. The original data file can be found in data/diabetic_data.csv.
The processed dataset we use is located in data/diabetic_preprocessed.csv. If you want to further explore how we cleaned and processed the original dataset, you can refer to preprocess.py.
This tutorial was developed by a team of Fairlearn contributors. Fairlearn is an open-source, community-driven project to help data scientists improve fairness of AI systems. It includes a Python library for assessing and mitigating fairness-related harms, and various educational resources.
Fairlearn is built on top of popular Python data science libraries, such as pandas and scikit-learn.