| Does the whole exceed its parts? The effect of AI explanations on complementary team performance G Bansal, T Wu, J Zhou, R Fok, B Nushi, E Kamar, MT Ribeiro, D Weld CHI, 1-16, 2021 | 531 | 2021 |
| Beyond accuracy: The role of mental models in human-AI team performance G Bansal, B Nushi, E Kamar, WS Lasecki, DS Weld, E Horvitz HCOMP 7, 2-11, 2019 | 438 | 2019 |
| AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework Q Wu, G Bansal, J Zhang, Y Wu, B Li, EE Zhu, L Jiang, X Zhang, S Zhang, ... COLM, 2024 | 385 | 2024 |
| The challenge of crafting intelligible intelligence DS Weld, G Bansal Communications of the ACM 62 (6), 70-79, 2019 | 334* | 2019 |
| Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff G Bansal, B Nushi, E Kamar, DS Weld, WS Lasecki, E Horvitz AAAI 33 (01), 2429-2437, 2019 | 331 | 2019 |
| Is the most accurate AI the best teammate? Optimizing AI for teamwork G Bansal, B Nushi, E Kamar, E Horvitz, DS Weld AAAI 35 (13), 11405-11414, 2021 | 164 | 2021 |
| Reading between the lines: Modeling user behavior and costs in AI-assisted programming H Mozannar, G Bansal, A Fourney, E Horvitz CHI, 2024 | 87* | 2024 |
| Hierarchical summarization: Scaling up multi-document summarization J Christensen, S Soderland, G Bansal ACL, 902-912, 2014 | 77 | 2014 |
| Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations V Chen, QV Liao, JW Vaughan, G Bansal CSCW, 2023 | 62 | 2023 |
| A coverage-based utility model for identifying unknown unknowns G Bansal, D Weld AAAI 32 (1), 2018 | 43 | 2018 |
| Generation probabilities are not enough: Exploring the effectiveness of uncertainty highlighting in AI-powered code completions H Vasconcelos, G Bansal, A Fourney, QV Liao, JW Vaughan ToCHI, 2024 | 42* | 2024 |
| Do explanations help users detect errors in open-domain QA? An evaluation of spoken vs. visual explanations AV González, G Bansal, A Fan, Y Mehdad, R Jia, S Iyer ACL, 1103-1116, 2021 | 40* | 2021 |
| Emerging perspectives in human-centered machine learning G Ramos, J Suh, S Ghorashi, C Meek, R Banks, S Amershi, R Fiebrink, ... Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing …, 2019 | 23 | 2019 |
| Technology-enabled disinformation: Summary, lessons, and recommendations J Akers, G Bansal, G Cadamuro, C Chen, Q Chen, L Lin, P Mulcaire, ... arXiv preprint arXiv:1812.09383, 2018 | 23 | 2018 |
| When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming H Mozannar, G Bansal, A Fourney, E Horvitz AAAI, 2023 | 19* | 2023 |
| Aligning Offline Metrics and Human Judgments of Value for Code Generation Models V Dibia, A Fourney, G Bansal, F Poursabzi-Sangdeh, H Liu, S Amershi ACL, 2023 | 17* | 2023 |
| Data staining: A method for comparing faithfulness of explainers J Sippy, G Bansal, DS Weld Proc. of ICML Workshop on Human Interpretability in Machine Learning (WHI), 2020 | 12 | 2020 |
| Explanatory dialogs: Towards actionable, interactive explanations G Bansal Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 356-357, 2018 | 9 | 2018 |
| AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems V Dibia, J Chen, G Bansal, S Syed, A Fourney, E Zhu, C Wang, S Amershi arXiv preprint arXiv:2408.15247, 2024 | | 2024 |
| Trust and Reliance in Evolving Human-AI Workflows (TREW) Z Ashktorab, G Bansal, Z Buçinca, K Holstein, J Hullman, ... Extended Abstracts at CHI, 1-6, 2024 | | 2024 |
Daniel WeldAllen Institute for AI & University of WashingtonVerified email at cs.washington.edu