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Prior work has identified a resilient phenomenon that threatens the performance of human-AI decision-making teams: overreliance, when people agree with an AI, even when it is incorrect.
An Evaluation of the Human-Interpretability of Explanation
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
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The structure and function of explanations
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Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction
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Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
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Too much, too little, or just right? Ways explanations impact end users’ mental models. In 2013 IEEE Symposium on visual languages and human centric computing . IEEE, 3–10
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Reasoning about extreme events: A review of behavioural biases in relation to catastrophe risks
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What is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference
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Data-driven decisions for reducing readmissions for heart failure: General methodology and case study
Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , Vol. 7. 2–11
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Cicero: Multi-turn, contextual argumentation for accurate crowdsourcing. In Proceedings of the 2019 chi conference on human factors in computing systems . 1–14
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The impact of placebic explanations on trust in intelligent systems. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems . 1–6
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What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
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Mohsen Bayati, Mark Braverman, Michael Gillam, Karen M Mack, George Ruiz, Mark S Smith, and Eric Horvitz. 2014 · 2014
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The computational and neural basis of cognitive control: Charted territory and new frontiers
Matthew M Botvinick and Jonathan D Cohen. 2014 · 2014
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A labor/leisure tradeoff in cognitive control
Wouter Kool and Matthew Botvinick. 2014 · 2014
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National Security Through a Cockeyed Lens: How Cognitive Bias Impacts U.S. Foreign Policy by Steve A. Yetiv. Baltimore, MD, Johns Hopkins University Press, 2013. 168 pp. $24.95
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The role of explanations on trust and reliance in clinical decision support systems. In 2015 international conference on healthcare informatics . IEEE, 160–169
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. 2015 · 2015
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining . 1721–1730
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
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Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015 · 2015
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Trust in automation: Integrating empirical evidence on factors that influence trust
Kevin Anthony Hoff and Masooda Bashir. 2015 · 2015
Cited alongside, same era.
The principles and limits of algorithm-in-the-loop decision making
Ben Green and Yiling Chen. 2019 · 2019
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More human-likeness, more trust? The effect of anthropomorphism on self-reported and behavioral trust in continued and interdependent human-agent cooperation
Philipp Kulms and Stefan Kopp. 2019 · 2019
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Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–12
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On Human Predictions with Explanations and Predictions of Machine Learning Models
Vivian Lai and Chenhao Tan. 2019a · 2019
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Explanation of machine learning models using improved Shapley Additive Explanation. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics . 546–546
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A Slow Algorithm Improves Users’ Assessments of the Algorithm’s Accuracy
Joon Sung Park, Rick Barber, Alex Kirlik, and Karrie Karahalios. 2019 · 2019
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Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–15
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019 · 2019
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Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 chi conference on human factors in computing systems . 1–12
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019 · 2019
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Do I Trust My Machine Teammate? An Investigation from Perception to Decision. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19) . Association for Computing Machinery, New York, NY, USA, 460–468
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Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI ’20) . Association for Computing Machinery, New York, NY, USA, 454–464
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, and Elena L. Glassman. 2020 · 2020
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The Very Efficient Assessment of Need for Cognition: Developing a Six-Item Version
Gabriel Lins de Holanda Coelho, Paul H. P. Hanel, and Lukas J. Wolf. 2020 · 2020
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Human-centered explainable ai: Towards a reflective sociotechnical approach. In International Conference on Human-Computer Interaction . Springer, 449–466
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AI-mediated communication: definition, research agenda, and ethical considerations
Jeffrey T Hancock, Mor Naaman, and Karen Levy. 2020 · 2020
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Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
Peter Hase and Mohit Bansal. 2020 · 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–14
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Conceptual metaphors impact perceptions of human-AI collaboration
Pranav Khadpe, Ranjay Krishna, Li Fei-Fei, Jeffrey T Hancock, and Michael S Bernstein. 2020 · 2020
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To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making
Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos. 2021 · 2021
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Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
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The disagreement deconvolution: Bringing machine learning performance metrics in line with reality. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–14
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Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–14
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How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection
Maia Jacobs, Melanie F Pradier, Thomas H McCoy, Roy H Perlis, Finale Doshi-Velez, and Krzysztof Z Gajos. 2021b · 2021
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emmeans: Estimated Marginal Means, aka Least-Squares Means
Russell V. Lenth. 2021 · 2021
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. 2021 · 2021
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Evaluating XAI: A comparison of rule-based and example-based explanations
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21) . Association for Computing Machinery, New York, NY, USA, 318–328
Xinru Wang and Ming Yin. 2021 · 2021
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