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As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors.
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Human reliance on machine learning models when performance feedback is limited: Heuristics and risks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–16
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Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Proceedings of the 26th International Conference on Intelligent User Interfaces . 318–328
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How cognitive biases affect XAI-assisted decision-making: A systematic review. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 78–91
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Measuring progress on scalable oversight for large language models
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Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable AI. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , Vol. 10. 147–159
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Teaching models to express their uncertainty in words
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Should I follow AI-based advice? Measuring appropriate reliance in human-AI decision-making
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Uncalibrated models can improve human-ai collaboration
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Self-consistency improves chain of thought reasoning in language models
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Chain-of-thought prompting elicits reasoning in large language models
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Combating misinformation in the age of llms: Opportunities and challenges
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Understanding the role of human intuition on reliance in human-AI decision-making with explanations
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Investigating the impact of user trust on the adoption and use of ChatGPT: survey analysis
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Something Borrowed: Exploring the Influence of AI-Generated Explanation Text on the Composition of Human Explanations. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems . 1–7
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(A) I Am Not a Lawyer, But…: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice. In The 2024 ACM Conference on Fairness, Accountability, and Transparency . 2454–2469
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Enhancing AI-Assisted Group Decision Making through LLM-Powered Devil’s Advocate. In Proceedings of the 29th International Conference on Intelligent User Interfaces . 103–119
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Modulating Language Model Experiences through Frictions
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Kori Inkpen, Shreya Chappidi, Keri Mallari, Besmira Nushi, Divya Ramesh, Pietro Michelucci, Vani Mandava, Libuše Hannah Vepřek, and Gabrielle Quinn. 2023 · 2023
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Studying the effect of AI code generators on supporting novice learners in introductory programming. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–23
Majeed Kazemitabaar, Justin Chow, Carl Ka To Ma, Barbara J Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023
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When algorithms err: Differential impact of early vs. late errors on users’ reliance on algorithms
Antino Kim, Mochen Yang, and Jingjing Zhang. 2023 · 2023
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ChatGPT’s inconsistent moral advice influences users’ judgment
Sebastian Krügel, Andreas Ostermaier, and Matthias Uhl. 2023 · 2023
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Impact of guidance and interaction strategies for LLM use on Learner Performance and perception
Harsh Kumar, Ilya Musabirov, Mohi Reza, Jiakai Shi, Anastasia Kuzminykh, Joseph Jay Williams, and Michael Liut. 2023a · 2023
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Math Education with Large Language Models: Peril or Promise?
Harsh Kumar, David M Rothschild, Daniel G Goldstein, and Jake M Hofman. 2023b · 2023
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Ai transparency in the age of LLMs: A human-centered research roadmap
Q Vera Liao and Jennifer Wortman Vaughan. 2023 · 2023
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Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Vidhisha Balachandran, and Yulia Tsvetkov. 2024 · 2024
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From Text to Self: Users’ Perception of AIMC Tools on Interpersonal Communication and Self. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–17
Yue Fu, Sami Foell, Xuhai Xu, and Alexis Hiniker. 2024 · 2024
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A Decision Theoretic Framework for Measuring AI Reliance. In The 2024 ACM Conference on Fairness, Accountability, and Transparency . 221–236
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Interpretability Gone Bad: The Role of Bounded Rationality in How Practitioners Understand Machine Learning
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Debating with more persuasive llms leads to more truthful answers
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Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking
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