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Building trust in AI-based systems is deemed critical for their adoption and appropriate use.
Planning of experiments
David Roxbee Cox. 1958 · 1958
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Formalising trust as a computational concept
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Foundations for an Empirically Determined Scale of Trust in Automated Systems
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Familiarity, Confidence, Trust: Problems and Alternatives
Niklas Luhmann. 2000 · 2000
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The craft of research
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Trust in Automation: Designing for Appropriate Reliance
John D Lee and Katrina A See. 2004 · 2004
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Trust, untrust, distrust and mistrust–an exploration of the dark (er) side. In International conference on trust management . Springer, 17–33
Stephen Marsh and Mark R Dibben. 2005 · 2005
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Perception of risk: the Influence of General Trust, and General Confidence
Michael Siegrist, Heinz Gutscher, and Timothy C. Earle. 2005 · 2005
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Common Ground, Complex Problems and Decision Making
Pieter J. Beers, Henny P. A. Boshuizen, Paul A. Kirschner, and Wim H. Gijselaers. 2006 · 2006
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Treating socio-technical systems as engineering systems: some conceptual problems
Peter Kroes, Maarten Franssen, Ibo van de Poel, and Maarten Ottens. 2006 · 2006
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Designing for Flexible Interaction Between Humans and Automation: Delegation Interfaces for Supervisory Control
Christopher Allen Miller and Raja Parasuraman. 2007 · 2007
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Michel Grabisch, Jean-Luc Marichal, Radko Mesiar, and Endre Pap. 2009 · 2009
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Modelling trust in artificial agents, a first step toward the analysis of e-trust
Mariarosaria Taddeo. 2010 · 2010
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Traces of Digital Trust: An Interactive Design Perspective
Natasha Dwyer. 2011 · 2011
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Measuring trust in organisational research: Review and recommendations
Bill McEvily and Marco Tortoriello. 2011 · 2011
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A modal type theory for formalizing trusted communications
Giuseppe Primiero and Mariarosaria Taddeo. 2012 · 2012
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Trust-based specification of sociotechnical systems
Elda Paja, Amit K Chopra, and Paolo Giorgini. 2013 · 2013
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A survey on trust modeling
Jin-Hee Cho, Kevin Chan, and Sibel Adali. 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
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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 (KDD (San Francisco, California, USA). New York, NY, USA, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Explanations Considered Harmful? User Interactions with Machine Learning Systems
Dympna O’Sullivan Simone Stumpf, Adrian Bussone. 2016 · 2016
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How much to trust artificial intelligence?
George Hurlburt. 2017 · 2017
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Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec. 2017 · 2017
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Design and analysis of experiments
Douglas C Montgomery. 2017 · 2017
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Explaining explanations: An overview of interpretability of machine learning. In Proceedings of the IEEE 5th International Conference on data science and advanced analytics (DSAA) . 80–89
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018 · 2018
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Metrics for Explainable AI: Challenges and Prospects
R. Hoffman, S. Mueller, G. Klein, and J. Litman. 2018b · 2018
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Artificial intelligence in healthcare
Kun-Hsing Yu, Andrew L Beam, and Isaac S Kohane. 2018 · 2018
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Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance. In AAAI Conference on Human Computation and Crowdsourcing (HCOMP)
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S. Lasecki, Daniel S. Weld, and E. Horvitz. 2019 · 2019
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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 (Glasgow, Scotland Uk). New York, NY, USA, 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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Automated rationale generation: a technique for explainable AI and its effects on human perceptions. In Proceedings of the 24th International Conference on Intelligent User Interfaces . 263–274
Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark O Riedl. 2019 · 2019
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In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions
Andrea Ferrario, Michele Loi, and Eleonora Viganò. 2019 · 2019
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White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems.. In ICIS
Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch, and Patrick Zschech. 2020 · 2020
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’Let me explain!’: exploring the potential of virtual agents in explainable AI interaction design
Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber, and Elisabeth André. 2020 · 2020
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How do visual explanations foster end users’ appropriate trust in machine learning?
Fumeng Yang, Zhuanyi Huang, Jean Scholtz, and Dustin L. Arendt. 2020 · 2020
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Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain). New York, NY, USA, 295–305
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020 · 2020
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Ethics guidelines for trustworthy AI
AI Hleg. 2019 · 2019
Cited alongside, same era.
The global landscape of AI ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019 · 2019
Cited alongside, same era.
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller. 2019 · 2019
Cited alongside, same era.
How model accuracy and explanation fidelity influence user trust
Andrea Papenmeier, Gwenn Englebienne, and Christin Seifert. 2019 · 2019
Cited alongside, same era.
Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X
Nathan L. Tenhundfeld, Ewart J. de Visser, Kerstin Sophie Haring, Anthony J. Ries, Victor S. Finomore, and Chad C. Tossell. 2019 · 2019
Cited alongside, same era.
Unremarkable ai: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . 1–11
Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019 · 2019
Cited alongside, same era.
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Yin, Jennifer Wortman Vaughan, and Hanna M. Wallach. 2019 · 2019
Cited alongside, same era.
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta, Natalia Diaz-Rodriguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020 · 2020
Cited alongside, same era.
Interacting with Explanations through Critiquing. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 , Zhi-Hua Zhou (Ed.). International Joint Conferences on Artificial Intelligence Organization, 515–521
Diego Antognini, Claudiu Musat, and Boi Faltings. 2021 · 2021
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang (Sherry) Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Túlio Ribeiro, and Daniel S. Weld. 2021 · 2021
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Trusting Automation: Designing for Responsivity and Resilience
Erin K. Chiou and John D. Lee. 2021 · 2021
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Can You Trust Your Trust Measure?
Meia Chita-Tegmark, Theresa Law, Nicholas Rabb, and Matthias Scheutz. 2021 · 2021
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Expanding Explainability: Towards Social Transparency in AI systems
Upol Ehsan, Qingzi Vera Liao, Michael J. Muller, Mark O. Riedl, and Justin D. Weisz. 2021 · 2021
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Trust Does Not Need to Be Human: It Is Possible to Trust Medical AI
Andrea Ferrario, Michele Loi, and Eleonora Viganò. 2021 · 2021
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Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)
Janet Hsiao, Hilary Hei Ting Ngai, Luyu Qiu, Yi Yang, and Caleb Chen Cao. 2021 · 2021
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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller, and Y. Goldberg. 2021 · 2021
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On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness
Lena Kastner, Markus Langer, Veronika Lazar, Astrid Schomacker, Timo Speith, and Sarah Sterz. 2021 · 2021
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How Should Intelligent Agents Apologize to Restore Trust? Interaction Effects Between Anthropomorphism and Apology Attribution on Trust Repair
Taenyun Kim and Hayeon Song. 2021 · 2021
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The Sanction of Authority: Promoting Public Trust in AI
Bran Knowles and John T. Richards. 2021 · 2021
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What Do We Want From Explainable Artificial Intelligence (XAI)? - A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva Schmidt, Andreas Sesing, and Kevin Baum. 2021 · 2021
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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
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Transparency as design publicity: explaining and justifying inscrutable algorithms
Michele Loi, Andrea Ferrario, and Eleonora Viganò. 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 . 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
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna M. Wallach. 2021 · 2021
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Initial evidence for biased decision-making despite human-centered AI explanation. In CHI 2021 Workshop: Operationalizing Human-Centered Perspectives in Explainable AI . Association for Computing Machinery, Yokohama, Japan, 1–5
N. Scharowski, N. Opwis, and F. Bruhlmann. [n. d.] · 2021
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Towards Warranted Trust: A Model on the Relation Between Actual and Perceived System Trustworthiness
Nadine Schlicker and Markus Langer. 2021 · 2021
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The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI
Donghee Shin. 2021 · 2021
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How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
Oleksandra Vereschak, Gilles Bailly, and Baptiste Caramiaux. 2021 · 2021
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Development and validation of a deep learning model using convolutional neural networks to identify scaphoid fractures in radiographs
Alfred P Yoon, Yi-Lun Lee, Robert L Kane, Chang-Fu Kuo, Chihung Lin, and Kevin C Chung. 2021 · 2021
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Dictionary by Merriam-Webster: America’s most-trusted online dictionary
2022 · 2022
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How Explainability Contributes to Trust in AI
Andrea Ferrario and Michele Loi. 2022 · 2022
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What is it like to trust a rock? A functionalist perspective on trust and trustworthiness in artificial intelligence
Peter R. Lewis and Stephen Marsh. 2022 · 2022
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Let’s Think Together! Assessing Shared Mental Models, Performance, and Trust in Human-Agent Teams
Beau G. Schelble, Christopher Flathmann, Nathan J. Mcneese, Guo Freeman, and Rohit Mallick. 2022 · 2022
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