Presentation: Create a Fair & transparent AI Pipeline with AI Fairness 360

One of the most critical and controversial topics around artificial intelligence centers around bias. As more apps come to market that rely on artificial intelligence, software developers and data scientists can unwittingly (or perhaps even knowingly) inject their personal biases into these solutions.

Because flaws and biases may not be easy to detect without the right tool, we have launched AI Fairness 360, an open source library to help detect and remove bias in machine learning models and data sets.

The AI Fairness 360 Python package includes a comprehensive set of metrics for data sets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in data sets and models. The research community worked together to create 30 fairness metrics and nine state-of-the-art bias mitigation algorithms.

We will share lessons learned while using AI Fairness 360 and demonstrate how to leverage it to detect and de bias models during pre-processing, in-processing, and post-processing. We will explain how to take these practices and apply them on training in on a more robust environment using Fabric for Deep Learning (FfDL, pronounced “fiddle”) which provides a consistent way to run various scalable deep learning frameworks as a service on Kubernetes.