A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications

no code implementations • 26 Oct 2023 • Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan, Chad Atalla, Emily Sheng, Dan Vann, Richard Edgar, Hamid Palangi, Roman Lutz, Hongliang Kong, Vincent Yun, Eslam Kamal, Federico Zarfati, Hanna Wallach, Sarah Bird, Mei Chen

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services.

Fairlearn: Assessing and Improving Fairness of AI Systems

no code implementations • 29 Mar 2023 • Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems.

Fairness

NFL Play Prediction

1 code implementation • 4 Jan 2016 • Brendan Teich, Roman Lutz, Valentin Kassarnig

Based on NFL game data we try to predict the outcome of a play in multiple different ways.

Fantasy Football Prediction

no code implementations • 26 May 2015 • Roman Lutz

The ubiquity of professional sports and specifically the NFL have lead to an increase in popularity for Fantasy Football.