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Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration.
Model selection
Linhart, H. and Zucchini, W · 1986
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Does biology constrain culture?
Rogers, A. R · 1988
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Delegating to software agents
Milewski, A. E. and Lewis, S. H · 1997
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Choosing not to choose
Sunstein, C. R · 2014
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A taxonomy for task allocation problems with temporal and ordering constraints
Nunes, E., Manner, M., Mitiche, H., and Gini, M · 2017
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Designing fair, efficient, and interpretable policies for prioritizing homeless youth for housing resources
Azizi, M. J., Vayanos, P., Wilder, B., Rice, E., and Tambe, M · 2018
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Online learning with abstention
Cortes, C., DeSalvo, G., Gentile, C., Mohri, M., and Yang, S · 2018
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Predict responsibly: Improving fairness and accuracy by learning to defer, 2018
Madras, D., Pitassi, T., and Zemel, R · 2018
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Ask not what ai can do, but what ai should do: Towards a framework of task delegability
Lubars, B. and Tan, C · 2019
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D. X., and Steinhardt, J · 2020
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Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D · 2020
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Does the whole exceed its parts? the effect of AI explanations on complementary team performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., and Weld, D · 2021
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Human-AI collaboration with bandit feedback
Gao, R., Saar-Tsechansky, M., De-Arteaga, M., Han, L., Lee, M. K., and Lease, M · 2021
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Towards unbiased and accurate deferral to multiple experts
Keswani, V., Lease, M., and Kenthapadi, K · 2021
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Scaling vision with sparse mixture of experts
Riquelme, C., Puigcerver, J., Mustafa, B., Neumann, M., Jenatton, R., Susano Pinto, A., Keysers, D., and Houlsby, N · 2021
Cited alongside, same era.
Sample efficient learning of predictors that complement humans
Charusaie, M.-A., Mozannar, H., Sontag, D., and Samadi, S · 2022
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Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation
Fügener, A., Grahl, J., Gupta, A., and Ketter, W · 2022
Cited alongside, same era.
Learning complementary policies for human-ai teams
Gao, R., Saar-Tsechansky, M., De-Arteaga, M., Han, L., Sun, W., Lee, M. K., and Lease, M · 2022
Cited alongside, same era.
Managing uncertainty: An experiment on delegation and team selection
Hamman, J. R. and Martínez-Carrasco, M. A · 2023
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Human-ai co-orchestration: the role of artificial intelligence in orchestration
Holstein, K. and Olsen, J. K · 2023
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Check the box! how to deal with automation bias in ai-based personnel selection
Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., and Kubicek, B · 2023
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Towards a science of human-ai decision making: An overview of design space in empirical human-subject studies
Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., and Tan, C · 2023
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Llama: Open and efficient foundation language models
Touvron, H. et al · 2023
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Hemmer, P., Schellhammer, S., Vössing, M., Jakubik, J., and Satzger, G · 2022
Cited alongside, same era.
Human-ai collaboration via conditional delegation: A case study of content moderation
Lai, V., Carton, S., Bhatnagar, R., Liao, Q. V., Zhang, Y., and Tan, C · 2022
Cited alongside, same era.
Teaching humans when to defer to a classifier via exemplars
Mozannar, H., Satyanarayan, A., and Sontag, D · 2022
Cited alongside, same era.
Probabilistic machine learning: an introduction
Murphy, K. P · 2022
Cited alongside, same era.
Bayesian modeling of human–ai complementarity
Steyvers, M., Tejeda, H., Kerrigan, G., and Smyth, P · 2022
Cited alongside, same era.
Physicians’ preferences and willingness to pay for artificial intelligence-based assistance tools: a discrete choice experiment among german radiologists
von Wedel, P. and Hagist, C · 2022
Cited alongside, same era.
Learning Personalized Decision Support Policies
Bhatt, U., Chen, V., Collins, K. M., Kamalaruban, P., Kallina, E., Weller, A., and Talwalkar, A · 2023
Cited alongside, same era.
A decision theoretic framework for measuring ai reliance
Guo, Z., Wu, Y., Hartline, J. D., and Hullman, J · 2024
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Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al · 2024
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Navigating complexity: Orchestrated problem solving with multi-agent llms
Rasal, S. and Hauer, E · 2024
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Representational alignment supports effective machine teaching
Sucholutsky, I., Collins, K. M., Malaviya, M., Jacoby, N., Liu, W., Sumers, T. R., Korakakis, M., Bhatt, U., Ho, M., Tenenbaum, J. B., et al · 2024
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Accuracy-time tradeoffs in ai-assisted decision making under time pressure
Swaroop, S., Buçinca, Z., Gajos, K. Z., and Doshi-Velez, F · 2024
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Do llms exhibit human-like response biases? a case study in survey design
Tjuatja, L., Chen, V., Wu, T., Talwalkwar, A., and Neubig, G · 2024
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When combinations of humans and ai are useful: A systematic review and meta-analysis
Vaccaro, M., Almaatouq, A., and Malone, T · 2024
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Introducing computer use, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku
Anthropic Inc · 2025
Closest in time.
Revisiting rogers’ paradox in the context of human-ai interaction
Collins, K. M., Bhatt, U., and Sucholutsky, I · 2025
Closest in time.