17 citations · 27 across the 3 of their papers we have counts for
6 papers
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan +3
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve…
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…
A Case for Backward Compatibility for Human-AI Teams
Gagan Bansal, Besmira Nushi, Ece Kamar +3
AI systems are being deployed to support human decision making in high-stakes domains. In many cases, the human and AI form a team, in which the human makes decisions after reviewi…
Technology-Enabled Disinformation: Summary, Lessons, and Recommendations
John Akers, Gagan Bansal, Gabriel Cadamuro +12
Technology is increasingly used -- unintentionally (misinformation) or intentionally (disinformation) -- to spread false information at scale, with potentially broad-reaching socie…
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…