Data Skeptic cover image

Fairness Aware Outlier Detection

Data Skeptic

00:00

Experiments to Evaluate the Quality of the Models

We want a result that's not only useful, but also fair. This is like a regularization technique too. It takes a base lost function and it introduces a regularization with respect to the fairness constraints that we talked about catche. And let's get into some of the experiments, what sort of data set and test can you run to evaluate the quality of these results? To fustofa like when we do experiments in such new things, we don't really have bencma data sets, like where we just evaluate given metric andu r tan bi tatte. So here, to demonstrate the effect s, we started with very simple synthetic data, two-dimensional data

Transcript
Play full episode

The AI-powered Podcast Player

Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
App store bannerPlay store banner
Get the app