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The current literature on AI-advised decision making -- involving explainable AI systems advising human decision makers -- presents a series of inconclusive and confounding results.
A Human-Grounded Evaluation of SHAP for Alert Processing
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Collective Choice, Judgment, and Problem Solving
Garold Stasser and Beth Dietz-Uhler. 2001 · 2001
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Explaining Recommendations: Satisfaction vs. Promotion. In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces (IUI ’05) . Association for Computing Machinery, San Diego, CA, 1–6
Mustafa Bilgic and Raymond J. Mooney. 2005 · 2005
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Groups perform better than the best individuals on letters-to-numbers problems: effects of group size
Patrick R Laughlin, Erin C Hatch, Jonathan S Silver, and Lee Boh. 2006 · 2006
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The Structure and Function of Explanations
Tania Lombrozo. 2006 · 2006
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Thinking, fast and slow
Daniel Kahneman. 2011 · 2011
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Are Explanations Always Important? A Study of Deployed, Low-Cost Intelligent Interactive Systems. In Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces (Lisbon, Portugal) (IUI ’12) . Association for Computing Machinery, New York, NY, USA, 169–178
Andrea Bunt, Matthew Lount, and Catherine Lauzon. 2012 · 2012
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Groups perform better than the best individuals on letters-to-numbers problems: Effects of induced strategies
Harold R. Carey and Patrick R. Laughlin. 2012 · 2012
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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 (Sydney, NSW, Australia) (KDD ’15) . Association for Computing Machinery, New York, NY, USA, 1721–1730
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . Association for Computing Machinery, New York, NY, USA, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Human-Centric Justification of Machine Learning Predictions. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia, 1461–1467
Or Biran and Kathleen McKeown. 2017 · 2017
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada. 2018 · 2018
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Do Explanations Make VQA Models More Predictable to a Human?. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Brussels, Belgium, 1036–1042
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh. 2018 · 2018
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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Scott M. Lundberg, Bala Nair, Monica S. Vavilala, Mayumi Horibe, Michael J. Eisses, Trevor Adams, David E. Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, and Su-In Lee. 2018 · 2018
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Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S. Lasecki, Daniel S. Weld, and Eric Horvitz. 2019a · 2019
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Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, and Eric Horvitz. 2019b · 2019
Earlier work this paper cites.
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, Glasgow Scotland Uk, 1–12
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connell, Terrance Gray, F. Maxwell Harper, and Haiyi Zhu. 2019 · 2019
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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 (Glasgow, Scotland Uk) (CHI EA ’19) . Association for Computing Machinery, New York, NY, USA, 1–6
Malin Eiband, Daniel Buschek, Alexander Kremer, and Heinrich Hussmann. 2019 · 2019
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What Can AI Do for Me? Evaluating Machine Learning Interpretations in Cooperative Play. 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, 229–239
Shi Feng and Jordan Boyd-Graber. 2019 · 2019
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The Principles and Limits of Algorithm-in-the-Loop Decision Making
Ben Green and Yiling Chen. 2019 · 2019
Cited alongside, same era.
Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?
Benjamin D. Horne, Dorit Nevo, John O’Donovan, Jin-Hee Cho, and Sibel Adalı. 2019 · 2019
Cited alongside, same era.
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 29–38
Vivian Lai and Chenhao Tan. 2019 · 2019
Cited alongside, same era.
Explanation in Artificial Intelligence: Insights From the Social Sciences
Tim Miller. 2019 · 2019
Cited alongside, same era.
Explaining Explanations in AI. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 279–288
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 . Association for Computational Linguistics, Online, 1103–1116
Ana Valeria González, Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, and Srinivasan Iyer. 2021 · 2021
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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. 2021 · 2021
Later among the works it cites.
Aligning Faithful Interpretations with their Social Attribution
Alon Jacovi and Yoav Goldberg. 2021 · 2021
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How Can I Choose An Explainer? An Application-grounded Evaluation of Post-hoc Explanations. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21) . Association for Computing Machinery, New York, NY, USA, 805–815
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, and João Gama. 2021 · 2021
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Brent Mittelstadt, Chris Russell, and Sandra Wachter. 2019 · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
The challenge of crafting intelligible intelligence
Daniel S. Weld and Gagan Bansal. 2019 · 2019
Cited alongside, same era.
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
Kun Yu, Shlomo Berkovsky, Ronnie Taib, Jianlong Zhou, and Fang Chen. 2019 · 2019
Cited alongside, same era.
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study. In Proceedings of the 25th International Conference on Intelligent User Interfaces (Cagliari, Italy) (IUI ’20) . Association for Computing Machinery, New York, NY, USA, 275–285
Ahmed Alqaraawi, Martin Schuessler, Philipp Weiß, Enrico Costanza, and Nadia Berthouze. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Feature-Based Explanations Don’t Help People Detect Misclassifications of Online Toxicity
Samuel Carton, Qiaozhu Mei, and Paul Resnick. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Han Liu, Vivian Lai, and Chenhao Tan. 2021 · 2021
Later among the works it cites.
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen. 2021 · 2021
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Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In 26th International Conference on Intelligent User Interfaces . ACM, College Station TX USA, 340–350
Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate. 2021 · 2021
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Manipulating and Measuring Model Interpretability. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 237, 52 pages
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach. 2021 · 2021
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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 . ACM, College Station TX USA, 318–328
Xinru Wang and Ming Yin. 2021 · 2021
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HILDIF: Interactive Debugging of NLI Models Using Influence Functions. In Proceedings of the First Workshop on Interactive Learning for Natural Language Processing . Association for Computational Linguistics, Online, 1–6
Hugo Zylberajch, Piyawat Lertvittayakumjorn, and Francesca Toni. 2021 · 2021
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It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 248–266
Andrew Bell, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich. 2022 · 2022
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Measuring Progress on Scalable Oversight for Large Language Models
Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamilė Lukošiūtė, Amanda Askell, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Christopher Olah, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Jackson Kernion, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Liane Lovitt, Nelson Elhage, Nicholas Schiefer, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Robin Larson, Sam McCandlish, Sandipan Kundu, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Ben Mann, and Jared Kaplan. 2022 · 2022
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Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22) . Association for Computing Machinery, New York, NY, USA, 794–806
Krzysztof Z. Gajos and Lena Mamykina. 2022 · 2022
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HIVE: Evaluating the Human Interpretability of Visual Explanations. In Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XII (Tel Aviv, Israel). Springer-Verlag, Berlin, Heidelberg, 280–298
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky. 2022 · 2022
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Evaluating Human-Language Model Interaction
Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, and Percy Liang. 2022 · 2022
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Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions. In Proceedings of the First Workshop on Learning with Natural Language Supervision . Association for Computational Linguistics, Dublin, Ireland, 17–28
Alicia Parrish, Harsh Trivedi, Ethan Perez, Angelica Chen, Nikita Nangia, Jason Phang, and Samuel Bowman. 2022 · 2022
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Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Mohammad Reza Taesiri, Giang Nguyen, and Anh Nguyen. 2022 · 2022
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On the Diversity and Limits of Human Explanations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Seattle, United States, 2173–2188
Chenhao Tan. 2022 · 2022
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Machine Explanations and Human Understanding. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 1
Chacha Chen, Shi Feng, Amit Sharma, and Chenhao Tan. 2023a · 2023
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Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, and Gagan Bansal. 2023b · 2023
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"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 250, 17 pages
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, and Andrés Monroy-Hernández. 2023b · 2023
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Explainable AI is Dead, Long Live Explainable AI! Hypothesis-Driven Decision Support Using Evaluative AI. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23) . Association for Computing Machinery, New York, NY, USA, 333–342
Tim Miller. 2023 · 2023
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Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations. In Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23) . Association for Computing Machinery, New York, NY, USA, 410–422
Max Schemmer, Niklas Kuehl, Carina Benz, Andrea Bartos, and Gerhard Satzger. 2023 · 2023
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Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 754, 18 pages
Venkatesh Sivaraman, Leigh A Bukowski, Joel Levin, Jeremy M. Kahn, and Adam Perer. 2023 · 2023
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Explanations Can Reduce Overreliance on AI Systems During Decision-Making
Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael S Bernstein, and Ranjay Krishna. 2023 · 2023
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