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AI systems are being deployed to support human decision making in high-stakes domains.
Mental models in human-computer interaction
Carroll, J. M. and Olson, J. R · 1988
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The psychology of everyday things
Norman, D · 1988
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The role of mental models in team performance in complex systems
Rouse, W. B., Cannon-Bowers, J. A., and Salas, E · 1992
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Collaborative systems (AAAI-94 presidential address)
Grosz, B. J · 1996
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The evolution of sharedplans
Grosz, B. J. and Kraus, S · 1999
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Algorithmic stability and generalization performance
Bousquet, O. and Elisseeff, A · 2001
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The foundations of cost-sensitive learning
Elkan, C · 2001
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Learning with local and global consistency
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Schölkopf, B · 2004
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Techniques for maintaining compatibility of a software core module and an interacting module, November 29 2005
Spring, M. J · 2005
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From software product lines to software ecosystems
Bosch, J · 2009
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The cognitive underpinnings of effective teamwork: A meta-analysis
DeChurch, L. A. and Mesmer-Magnus, J. R · 2010
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Combining human and machine intelligence in large-scale crowdsourcing
Kamar, E., Hacker, S., and Horvitz, E · 2012
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Tell me more?: the effects of mental model soundness on personalizing an intelligent agent
Kulesza, T., Stumpf, S., Burnett, M., and Kwan, I · 2012
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Do deep nets really need to be deep?
Ba, J. and Caruana, R · 2014
Cited alongside, same era.
Data-driven decisions for reducing readmissions for heart failure: General methodology and case study
Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., and Horvitz, E · 2014
Cited alongside, same era.
Some observations on mental models
Norman, D. A · 2014
The effects of automatic speech recognition quality on human transcription latency
Gaur, Y., Lasecki, W. S., Metze, F., and Bigham, J. P · 2016
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MIMIC-III, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Li-wei, H. L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., and Mark, R. G · 2016
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Directions in hybrid intelligence: Complementing AI systems with human intelligence
Kamar, E · 2016
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Deep learning for identifying metastatic breast cancer
Wang, D., Khosla, A., Gargeya, R., Irshad, H., and Beck, A. H · 2016
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Multitask learning and benchmarking with clinical time series data
Harutyunyan, H., Khachatrian, H., Kale, D. C., and Galstyan, A · 2017
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
Cited alongside, same era.
Machine bias: There’s software across the country to predict future criminals and it’s biased against blacks
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Cited alongside, same era.
Real-time captioning by groups of non-experts
Lasecki, W., Miller, C., Sadilek, A., Abumoussa, A., Borrello, D., Kushalnagar, R., and Bigham, J
Cited in the paper.
Real-time collaborative planning with the crowd
Lasecki, W. S., Bigham, J. P., Allen, J. F., and Ferguson, G
Cited in the paper.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al · 2017
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Effects of uncertainty and cognitive load on user trust in predictive decision making
Zhou, J., Arshad, S. Z., Luo, S., and Chen, F · 2017
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Tesla can change so much with over-the-air updates that it’s messing with some owners’ heads
O’Cane, S · 2018
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Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
Bansal, G., Nushi, B., Kamar, E., Weld, D., Lasecki, W. S., and Horvitz, E · 2019
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