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Language models are transforming the ways that their users engage with the world.
Learning mathematics from examples and by doing
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Inside the nudge unit: How small changes can make a big difference
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How effective is nudging? a quantitative review on the effect sizes and limits of empirical nudging studies
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Algorithm appreciation: People prefer algorithmic to human judgment
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L. Han · 2020
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Selective classification can magnify disparities across groups
E. Jones, S. Sagawa, P. W. Koh, A. Kumar, and P. Liang · 2020
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Consistent estimators for learning to defer to an expert
H. Mozannar and D. Sontag · 2020
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A. T. Schmidt and B. Engelen · 2020
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Learning personalized decision support policies
U. Bhatt, V. Chen, K. M. Collins, P. Kamalaruban, E. Kallina, A. Weller, and A. Talwalkar · 2023
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Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures
F. Callaway, M. Hardy, and T. L. Griffiths · 2023
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Understanding the role of human intuition on reliance in human-ai decision-making with explanations
V. Chen, Q. V. Liao, J. Wortman Vaughan, and G. Bansal · 2023
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Mathematical capabilities of chatgpt
S. Frieder, L. Pinchetti, R. Griffiths, T. Salvatori, P. Lukasiewicz, T. Petersen, A. Chevalier, and J. Berner · 2023
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How close is chatgpt to human experts? comparison corpus, evaluation, and detection
B. Guo, X. Zhang, Z. Wang, M. Jiang, J. Nie, Y. Ding, J. Yue, and Y. Wu · 2023
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To trust or to think: Cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making
Z. Buçinca, M. B. Malaya, and K. Z. Gajos · 2021
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To trust or to think: cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making
Z. Buçinca, M. B. Malaya, and K. Z. Gajos · 2021
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Measuring massive multitask language understanding
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt · 2021
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Towards a science of human-ai decision making: a survey of empirical studies
V. Lai, C. Chen, Q. V. Liao, A. Smith-Renner, and C. Tan · 2021
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Nudging and design friction: The impact on our decision making process
E. Sundin · 2021
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Human-AI collaboration via conditional delegation: A case study of content moderation
V. Lai, S. Carton, R. Bhatnagar, Q. V. Liao, Y. Zhang, and C. Tan · 2022
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Are machine rationales (not) useful to humans? measuring and improving human utility of free-text rationales
B. Joshi, Z. Liu, S. Ramnath, A. Chan, Z. Tong, S. Nie, Q. Wang, Y. Choi, and X. Ren · 2023
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Who should i trust: Ai or myself? leveraging human and ai correctness likelihood to promote appropriate trust in ai-assisted decision-making
S. Ma, Y. Lei, X. Wang, C. Zheng, C. Shi, M. Yin, and X. Ma · 2023
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Effective human-ai teams via learned natural language rules and onboarding
H. Mozannar, J. J. Lee, D. Wei, P. Sattigeri, S. Das, and D. Sontag · 2023
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University students as early adopters of chatgpt: Innovation diffusion study
R. Raman, S. Mandal, P. Das, and a. et · 2023
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An analysis of the automatic bug fixing performance of chatgpt
D. Sobania, M. Briesch, C. Hanna, and J. Petke · 2023
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Explanations can reduce overreliance on ai systems during decision-making
H. Vasconcelos, M. Jörke, M. Grunde-McLaughlin, T. Gerstenberg, M. S. Bernstein, and R. Krishna · 2023
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When should algorithms resign?
U. Bhatt and H. Sargeant · 2024
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Z. Buçinca, S. Swaroop, A. E. Paluch, S. A. Murphy, and K. Z. Gajos · 2024
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The benefits, risks and bounds of personalizing the alignment of large language models to individuals
H. R. Kirk, B. Vidgen, P. Röttger, and S. A. Hale · 2024
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Decoding ai’s nudge: A unified framework to predict human behavior in ai-assisted decision making
Z. Li, Z. Lu, and M. Yin · 2024
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Effective human-ai teams via learned natural language rules and onboarding
H. Mozannar, J. Lee, D. Wei, P. Sattigeri, S. Das, and D. Sontag · 2024
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Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn · 2024
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