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With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes.
Cognitive Reflection and Decision Making
Frederick, S. 2005 · 2005
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” Why should i trust you?” Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
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A unified approach to interpreting model predictions
Lundberg, S. M.; and Lee, S.-I. 2017 · 2017
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Predicting human behavior: The next frontiers
Subrahmanian, V.; and Kumar, S. 2017 · 2017
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What can AI do for me?: evaluating machine learning interpretations in cooperative play
Feng, S.; and Boyd-Graber, J. L. 2018 · 2018
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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. 2018 · 2018
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Explanations as Mechanisms for Supporting Algorithmic Transparency
Rader, E. J.; Cotter, K.; and Cho, J. 2018 · 2018
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Predictive model to assess user trust: a psycho-physiological approach
Ajenaghughrure, I. B.; Sousa, S. C.; Kosunen, I. J.; and Lamas, D. 2019 · 2019
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Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services
Brown, A.; Chouldechova, A.; Putnam-Hornstein, E.; Tobin, A.; and Vaithianathan, R. 2019 · 2019
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The effects of example-based explanations in a machine learning interface
Cai, C. J.; Jongejan, J.; and Holbrook, J. 2019 · 2019
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Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, C. J.; Reif, E.; Hegde, N.; Hipp, J.; Kim, B.; Smilkov, D.; Wattenberg, M.; Viegas, F.; Corrado, G. S.; Stumpe, M. C.; and Terry, M. 2019a · 2019
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Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, C. J.; Reif, E.; Hegde, N.; Hipp, J. D.; Kim, B.; Smilkov, D.; Wattenberg, M.; Viégas, F. B.; Corrado, G. S.; Stumpe, M. C.; and Terry, M. 2019b · 2019
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Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders
Cheng, H. F.; Wang, R.; Zhang, Z.; O’Connell, F.; Gray, T.; Harper, F. M.; and Zhu, H. 2019 · 2019
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Explaining models: an empirical study of how explanations impact fairness judgment
Dodge, J.; Liao, Q. V.; Zhang, Y.; Bellamy, R. K. E.; and Dugan, C. 2019 · 2019
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Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing
Grgic-Hlaca, N.; Engel, C.; and Gummadi, K. P. 2019 · 2019
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Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing
Grgić-Hlača, N.; Engel, C.; and Gummadi, K. P. 2019 · 2019
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Visualizing Uncertainty and Alternatives in Event Sequence Predictions
Guo, S.; Du, F.; Malik, S.; Koh, E.; Kim, S.; Liu, Z.; Kim, D.; Zha, H.; and Cao, N. 2019 · 2019
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Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems
Kocielnik, R.; Amershi, S.; and Bennett, P. N. 2019 · 2019
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Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems
Kunkel, J.; Donkers, T.; Michael, L.; Barbu, C.-M.; and Ziegler, J. 2019 · 2019
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Procedural Justice in Algorithmic Fairness: Leveraging Transparency and Outcome Control for Fair Algorithmic Mediation
Lee, M. K.; Jain, A.; Cha, H. J.; Ojha, S.; and Kusbit, D. 2019 · 2019
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A Slow Algorithm Improves Users’ Assessments of the Algorithm’s Accuracy
Park, J. S.; Berlin, R. B.; Kirlik, A.; and Karahalios, K. 2019 · 2019
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A Markovian Method for Predicting Trust Behavior in Human-Agent Interaction
Pynadath, D. V.; Wang, N.; and Kamireddy, S. 2019 · 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Yin, M.; Vaughan, J. W.; and Wallach, H. M. 2019 · 2019
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Do I trust my machine teammate?: an investigation from perception to decision
Yu, K.; Berkovsky, S.; Taib, R.; Zhou, J.; and Chen, F. 2019 · 2019
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COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A.; von der Weth, C.; Kankanhalli, M. S.; and Lim, B. Y. 2020 · 2020
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Evaluating saliency map explanations for convolutional neural networks: a user study
Alqaraawi, A.; Schuessler, M.; Weiß, P.; Costanza, E.; and Bianchi-Berthouze, N. 2020 · 2020
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Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
Buccinca, Z.; Lin, P.; Gajos, K. Z.; and Glassman, E. L. 2020 · 2020
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Leveraging rationales to improve human task performance
Das, D.; and Chernova, S. 2020 · 2020
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Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
Levy, A.; Agrawal, M.; Satyanarayan, A.; and Sontag, D. A. 2021b · 2021
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Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Liu, H.; Lai, V.; and Tan, C. 2021 · 2021
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Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks
Lu, Z.; and Yin, M. 2021 · 2021
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Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems
Nourani, M.; Roy, C.; Block, J. E.; Honeycutt, D. R.; Rahman, T.; Ragan, E. D.; and Gogate, V. 2021 · 2021
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Manipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F.; Goldstein, D. G.; Hofman, J. M.; Vaughan, J. W.; and Wallach, H. M. 2018 · 2021
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De-Arteaga, M.; Fogliato, R.; and Chouldechova, A. 2020 · 2020
Cited alongside, same era.
ViCE: visual counterfactual explanations for machine learning models
Gomez, O.; Holter, S.; Yuan, J.; and Bertini, E. 2020 · 2020
Cited alongside, same era.
An empirical study on the perceived fairness of realistic, imperfect machine learning models
Harrison, G.; Hanson, J.; Jacinto, C.; Ramirez, J.; and Ur, B. 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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Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment
Lee, M. H.; Siewiorek, D. P.; Smailagic, A.; Bernardino, A.; and i Badia, S. B. 2020 · 2020
Cited alongside, same era.
Why does my model fail?: contrastive local explanations for retail forecasting
Lucic, A.; Haned, H.; and de Rijke, M. 2019 · 2020
Cited alongside, same era.
No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML
Smith-Renner, A.; Fan, R.; Birchfield, M. K.; Wu, T. S.; Boyd-Graber, J. L.; Weld, D. S.; and Findlater, L. 2020 · 2020
Cited alongside, same era.
Visual, textual or hybrid: the effect of user expertise on different explanations
Szymanski, M.; Millecamp, M.; and Verbert, K. 2021 · 2021
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Exploring and Promoting Diagnostic Transparency and Explainability in Online Symptom Checkers
Tsai, C.-H.; You, Y.; Gui, X.; Kou, Y.; and Carroll, J. M. 2021 · 2021
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Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors
van Berkel, N.; Gonçalves, J.; Russo, D.; Hosio, S. J.; and Skov, M. B. 2021 · 2021
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
Wang, X.; and Yin, M. 2021 · 2021
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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. E. 2020 · 2021
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Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures
Callaway, F.; Hardy, M.; and Griffiths, T. 2022 · 2022
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Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
Fogliato, R.; Chappidi, S.; Lungren, M. P.; Fitzke, M.; Parkinson, M.; Wilson, D. U.; Fisher, P.; Horvitz, E.; Inkpen, K.; and Nushi, B. 2022 · 2022
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Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
Gajos, K. Z.; and Mamykina, L. 2022 · 2022
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Overreliance on AI Literature Review
Passi, S.; and Vorvoreanu, M. 2022 · 2022
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Human Interpretation of Saliency-based Explanation Over Text
Schuff, H.; Jacovi, A.; Adel, H.; Goldberg, Y.; and Vu, N. T. 2022 · 2022
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AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies
Tejeda, H.; Kumar, A.; Smyth, P.; and Steyvers, M. 2022 · 2022
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Will you accept the ai recommendation? predicting human behavior in ai-assisted decision making
Wang, X.; Lu, Z.; and Yin, M. 2022 · 2022
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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 · 2023
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Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach
Li, Z.; Lu, Z.; and Yin, M. 2023 · 2023
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Strategic adversarial attacks in AI-assisted decision making to reduce human trust and reliance
Lu, Z.; Li, Z.; Chiang, C.-W.; and Yin, M. 2023 · 2023
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Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
Ma, S.; Lei, Y.; Wang, X.; Zheng, C.; Shi, C.; Yin, M.; and Ma, X. 2023 · 2023
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Diabetes Prediction Dataset
Mustafatz. 2023 · 2023
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The effects of AI biases and explanations on human decision fairness: a case study of bidding in rental housing markets
Wang, X.; Liang, C.; and Yin, M. 2023 · 2023
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