31 citations · 90 across the 9 of their papers we have counts for
3 papers · 1 filter
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
Jeff Druce, James Niehaus, Vanessa Moody +2
The advances in artificial intelligence enabled by deep learning architectures are undeniable. In several cases, deep neural network driven models have surpassed human level perfor…
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Amanda Gentzel, Dan Garant, David Jensen
Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An ac…
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
John Foley, Emma Tosch, Kaleigh Clary +1
It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agent…