584 citations · 1.3k across the 49 of their papers we have counts for
11 papers · 1 filter
In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making
Raymond Fok, Daniel S. Weld
The current literature on AI-advised decision making -- involving explainable AI systems advising human decision makers -- presents a series of inconclusive and confounding results…
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou +5
Many researchers motivate explainable AI with studies showing that human-AI team performance on decision-making tasks improves when the AI explains its recommendations. However, pr…
Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork
Gagan Bansal, Besmira Nushi, Ece Kamar +2
AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI system…
The Challenge of Crafting Intelligible Intelligence
Daniel S. Weld, Gagan Bansal
Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results…
A Programming Language With a POMDP Inside
Christopher H. Lin, Mausam, Daniel S. Weld
We present POAPS, a novel planning system for defining Partially Observable Markov Decision Processes (POMDPs) that abstracts away from POMDP details for the benefit of non-expert…
Topological Value Iteration Algorithms
Peng Dai, Mausam, Daniel Sabby Weld +1
Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, whic…