21 citations · 77 across the 10 of their papers we have counts for
11 papers
Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi +5
Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment…
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Hussein Mozannar, Gagan Bansal, Adam Fourney +1
AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim of improving produ…
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan +1
AI explanations are often mentioned as a way to improve human-AI decision-making, but empirical studies have not found consistent evidence of explanations' effectiveness and, on th…
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models
Victor Dibia, Adam Fourney, Gagan Bansal +3
Large language models have demonstrated great potential to assist programmers in generating code. For such human-AI pair programming scenarios, we empirically demonstrate that whil…
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Hussein Mozannar, Gagan Bansal, Adam Fourney +1
Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully real…
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…