Gods and Robots In this episode of the podcast we shake things up! Neil is on the guest side of the table with his partner Rabbi Laura Janner-Klausner to discuss their upcoming project Gods and Robots. Katherine is joined on the host side by friend of the show professor Michael Littman. See... See More Episodes arXiv Whitepapers An Empirical Analysis of Racial Categories in the Algorithmic Fairness Literature Recent work in algorithmic fairness has highlighted the challenge of defining racial categories for the purposes of anti-discrimination. These challenges are not new but have previously fallen to the state, which enacts race through government statistics, policies, and evidentiary standards in anti... Towards Understanding of Deepfake Videos in the Wild Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing datasets suffer from significant drawbacks that hamper their effectiveness. Notably, these datasets fail to encompass the... Towards User Guided Actionable Recourse Machine Learning's proliferation in critical fields such as healthcare, banking, and criminal justice has motivated the creation of tools which ensure trust and transparency in ML models. One such tool is Actionable Recourse (AR) for negatively impacted users. AR describes recommendations of cost... More featured content News Articles Artificial Intelligence for Precision Medicine and Better Healthcare Computer Vision Recipes: Best Practices and Examples Stay in the loop. Subscribe to our newsletter for a weekly update on the latest podcast, news, events, and jobs postings. E-mail Leave this field blank Making smart thermostats more efficient 4 ways to improve your TensorFlow model – key regularization techniques you need to know Want cheaper nuclear energy? Turn the design process into a game Method finds hidden warning signals in measurements collected over time 5 Different Ways to Load Data in Python How humans use objects in novel ways to solve problems It takes a lot of energy for machines to learn – here’s why AI is so power-hungry Building machines that better understand human goals More news