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Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems.
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence
Meehl, P. E · 1954
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On optimum recognition error and reject tradeoff
Chow, C. K · 1970
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Adversarial classification
Dalvi, N., Domingos, P., Sanghai, S., and Verma, D · 2004
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A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A · 2014
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Counterfactual risk minimization: Learning from logged bandit feedback
Swaminathan, A. and Joachims, T · 2015
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Machine bias risk assessments in criminal sentencing
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Burrell, J · 2016
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Learning with rejection
Cortes, C., DeSalvo, G., and Mohri, M · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
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How we analyzed the compas recidivism algorithm
Kirchner, L. and Larson, J · 2017
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The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables
Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., and Mullainathan, S · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Learning under selective labels in the presence of expert consistency
Consistent estimators for learning to defer to an expert
Mozannar, H. and Sontag, D · 2020
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Performative prediction
Perdomo, J., Zrnic, T., Mendler-Dünner, C., and Hardt, M · 2020
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Wilder, B., Horvitz, E., and Kamar, E · 2020
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Is the most accurate ai the best teammate? optimizing ai for teamwork
Bansal, G., Nushi, B., Kamar, E., Horvitz, E., and Weld, D. S · 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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How can I choose an explainer? An application-grounded evaluation of post-hoc explanations
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De-Arteaga, M., Dubrawski, A., and Chouldechova, A · 2018
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Predict responsibly: Improving fairness and accuracy by learning to defer
Madras, D., Pitassi, T., and Zemel, R · 2018
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Addressing failure prediction by learning model confidence
Corbière, C., Thome, N., Bar-Hen, A., Cord, M., and Pérez, P · 2019
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Yousefi, N., Alaghband, M., and Garibay, I · 2019
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Selective classification can magnify disparities across groups
Jones, E., Sagawa, S., Koh, P. W., Kumar, A., and Liang, P · 2020
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Jesus, S. M., Belém, C., Balayan, V., Bento, J., Saleiro, P., Bizarro, P., and Gama, J · 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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Designing closed human-in-the-loop deferral pipelines
Keswani, V., Lease, M., and Kenthapadi, K · 2022
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Calibrated learning to defer with one-vs-all classifiers
Verma, R. and Nalisnick, E · 2022
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