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Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications.
Rational choice and the structure of the environment
Simon, H. A. (1956) · 1956
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Theories of bounded rationality
Simon, H. A. (1972) · 1972
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Availability: A heuristic for judging frequency and probability
Tversky, A. and Kahneman, D. (1973) · 1973
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Judgment under uncertainty: Heuristics and biases
Tversky, A. and Kahneman, D. (1974) · 1974
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Cognitive biases and their impact on strategic planning
Barnes JR., J. H. (1984) · 1984
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Human-computer collaboration
Silverman, B. G. (1992) · 1992
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Varieties of confirmation bias
Klayman, J. (1995) · 1995
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Confirmation bias: A ubiquitous phenomenon in many guises
Nickerson, R. S. (1998) · 1998
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Cognitive biases and strategic decision processes: An integrative perspective
Das, T. and Teng, B.-S. (1999) · 1999
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Bayesian modeling of human concept learning
Tenenbaum, J. B. (1999) · 1999
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Putting adjustment back in the anchoring and adjustment heuristic: Differential processing of self-generated and experimenter-provided anchors
Epley, N. and Gilovich, T. (2001) · 2001
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Trust in automation: Designing for appropriate reliance
Lee, J. D. and See, K. A. (2004) · 2004
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Confirmation bias. In R. F. Pohl (Ed.). Cognitive Illusions. A Handbook on Fallacies and Biases in Thinking, Judgement and Memory
Oswald, M. and Grosjean, S. (2004) · 2004
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Cognitive biases and decision support systems development: a design science approach
Arnott, D. (2006) · 2006
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Probabilistic models of cognition: Conceptual foundations
Chater, N., Tenenbaum, J. B., and Yuille, A. (2006) · 2006
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Playing dice with criminal sentences: The influence of irrelevant anchors on experts’ judicial decision making
Englich, B., Mussweiler, T., and Strack, F. (2006) · 2006
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The anchoring-and-adjustment heuristic: Why the adjustments are insufficient
Epley, N. and Gilovich, T. (2006) · 2006
Earlier work this paper cites.
Optimal predictions in everyday cognition
Griffiths, T. L. and Tenenbaum, J. B. (2006) · 2006
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Using data mining to predict secondary school student performance
Cortez, P. and Silva, A. M. G. (2008) · 2008
Earlier work this paper cites.
Addressing cognitive biases in augmented business decision systems
Baudel, T., Verbockhaven, M., Roy, G., Cousergue, V., and Laarach, R. (2020) · 2009
Earlier work this paper cites.
Barriers and Biases in Computer-Mediated Knowledge Communication: And How They May Be Overcome
Bromme, R., Hesse, F. W., and Spada, H. (2010) · 2010
Cited alongside, same era.
When good evidence goes bad: The weak evidence effect in judgment and decision-making
Fernbach, P., Darlow, A., and Sloman, S. (2011) · 2011
Cited alongside, same era.
A literature review of the anchoring effect
Furnham, A. and Boo, H. (2011) · 2011
Cited alongside, same era.
Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings
Payzan-LeNestour, E. and Bossaerts, P. (2011) · 2011
Cited alongside, same era.
Do not bet on the unknown versus try to find out more: Estimation uncertainty and “unexpected uncertainty” both modulate exploration
Payzan-LeNestour, E. and Bossaerts, P. (2012) · 2012
Cited alongside, same era.
Yeom, S. and Tschantz, M. C. (2018) · 2018
Later among the works it cites.
Factsheets: Increasing trust in ai services through supplier’s declarations of conformity
Arnold, M., Bellamy, R. K., Hind, M., Houde, S., Mehta, S., Mojsilović, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., et al. (2019) · 2019
Later among the works it cites.
Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., and Horvitz, E. (2019) · 2019
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The principles and limits of algorithm-in-the-loop decision making
Green, B. and Chen, Y. (2019) · 2019
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On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Lai, V. and Tan, C. (2019) · 2019
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Solomon, J. (2014) · 2014
Cited alongside, same era.
Big data system for analyzing risky procurement entities
Dhurandhar, A., Graves, B., Ravi, R. K., Maniachari, G., and Ettl, M. (2015) · 2015
Cited alongside, same era.
Designing information for remediating cognitive biases in decision-making
Zhang, Y., Bellamy, R. K., and Kellogg, W. A. (2015) · 2015
Cited alongside, same era.
Decision-making and cognitive biases
Ehrlinger, J., Readinger, W., and Kim, B. (2016) · 2016
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B. (2017) · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017) · 2017
Cited alongside, same era.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
Adadi, A. and Berrada, M. (2018) · 2018
Cited alongside, same era.
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · 2019
Later among the works it cites.
A slow algorithm improves users’ assessments of the algorithm’s accuracy
Park, J. S., Barber, R., Kirlik, A., and Karahalios, K. (2019) · 2019
Later among the works it cites.
Cognitive bias, decision styles, and risk attitudes in decision making and dss
Phillips-Wren, G., Power, D. J., and Mora, M. (2019) · 2019
Later among the works it cites.
Designing theory-driven user-centric explainable ai
Wang, D., Yang, Q., Abdul, A., and Lim, B. Y. (2019) · 2019
Later among the works it cites.
Comparison of markov versus quantum dynamical models of human decision making
Busemeyer, J. R., Kvam, P. D., and Pleskac, T. J. (2020) · 2020
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Information about action outcomes differentially affects learning from self-determined versus imposed choices
Chambon, V., Théro, H., Vidal, M., Vandendriessche, H., Haggard, P., and Palminteri, S. (2020) · 2020
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On cognitive preferences and the plausibility of rule-based models
Fürnkranz, J., Kliegr, T., and Paulheim, H. (2020) · 2020
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Applying collaborative cognitive load theory to computer-supported collaborative learning: towards a research agenda
Janssen, J. and Kirschner, P. (2020) · 2020
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Why is ‘chicago’ deceptive? towards building model-driven tutorials for humans
Lai, V., Liu, H., and Tan, C. (2020) · 2020
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Adaptive trust calibration for human-ai collaboration
Okamura, K. and Yamada, S. (2020) · 2020
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Rapid trust calibration through interpretable and uncertainty-aware ai
Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., and Kaplan, L. (2020) · 2020
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Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making
Zhang, Y., Liao, Q. V., and Bellamy, R. K. (2020) · 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) · 2021
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To trust or to think: Cognitive forcing functions canreduce overreliance on ai in ai-assisted decision-making
Buçinca, Z., Malaya, M. B., and Gajos, K. Z. (2021) · 2021
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