 Okay, you ready? Ready. So welcome. This is FairML and it came out of a Masters thesis project. The goal of FairML is to be able to audit and to develop an NTN toolbox to audit predictive models. Predictive models have become widely used to make consequential decisions like loans, housing, and determining access to different services. So our goal is to use FairML as an audit toolbox that people at the FTC or CFPB can use to basically audit predictive models and decisions that are coming out of predictive models in order to be able to identify whether these models are biased or not. And so this is the architecture of the system. It produces variable rankings that you can use to measure bias as well as a detailed report that you can read in order to be able to understand the analysis of our system. Thank you.