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In recent years, the rapid development of AI systems has brought about the benefits of intelligent services but also concerns about security and reliability.
Trust, self-confidence, and operators’ adaptation to automation
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Towards a theory of longitudinal trust calibration in human–robot teams
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Improving worker engagement through conversational microtask crowdsourcing. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . 1–12
Sihang Qiu, Ujwal Gadiraju, and Alessandro Bozzon. 2020 · 2020
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Can the crowd identify misinformation objectively? The effects of judgment scale and assessor’s background. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval . 439–448
Real ml: Recognizing, exploring, and articulating limitations of machine learning research. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency . 587–597
Jessie J Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan. 2022 · 2022
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Designing transparency for effective human-AI collaboration
Michael Vössing, Niklas Kühl, Matteo Lind, and Gerhard Satzger. 2022 · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In Proceedings of the 2022 CHI conference on human factors in computing systems . 1–22
Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022b · 2022
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Artificial Intelligence Risk Management Framework (AI RMF 1.0)
NIST AI. 2023 · 2023
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Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction
António Correia, Andrea Grover, Daniel Schneider, Ana Paula Pimentel, Ramon Chaves, Marcos Antonio De Almeida, and Benjamim Fonseca. 2023 · 2023
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Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, and Gianluca Demartini. 2020 · 2020
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" The human body is a black box" supporting clinical decision-making with deep learning. In Proceedings of the 2020 conference on fairness, accountability, and transparency . 99–109
Mark Sendak, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, and Cara O’Brien. 2020 · 2020
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Rapid trust calibration through interpretable and uncertainty-aware AI
Richard Tomsett, Alun Preece, Dave Braines, Federico Cerutti, Supriyo Chakraborty, Mani Srivastava, Gavin Pearson, and Lance Kaplan. 2020 · 2020
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020 , Mireille Hildebrandt, Carlos Castillo, L. Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 295–305
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020 · 2020
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Scaling up fact-checking using the wisdom of crowds
Jennifer Allen, Antonio A Arechar, Gordon Pennycook, and David G Rand. 2021 · 2021
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FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)
Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021 · 2021
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–16
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 · 2021
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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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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga, Américo Brilhante, Aslı Çakır, Lars Egevad, Martin Eklund, Xavier Farré, Katerina Geronatsiou, Vincent Molinié, et al · 2021
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In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making
Raymond Fok and Daniel S Weld. 2023 · 2023
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Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows
Madeleine Grunde-McLaughlin, Michelle S Lam, Ranjay Krishna, Daniel S Weld, and Jeffrey Heer. 2023 · 2023
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How Stated Accuracy of an AI System and Analogies to Explain Accuracy Affect Human Reliance on the System
Gaole He, Stefan Buijsman, and Ujwal Gadiraju. 2023a · 2023
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Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–18
Gaole He, Lucie Kuiper, and Ujwal Gadiraju. 2023b · 2023
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A systematic review of hate speech automatic detection using natural language processing
Md Saroar Jahan and Mourad Oussalah. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Trust in artificial intelligence: Meta-analytic findings
Alexandra D Kaplan, Theresa T Kessler, J Christopher Brill, and Peter A Hancock. 2023 · 2023
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Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies. 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, 1369–1385
Vivian Lai, Chacha Chen, Alison Smith-Renner, Q. Vera Liao, and Chenhao Tan. 2023 · 2023
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Trustworthy AI: From principles to practices
Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, and Bowen Zhou. 2023 · 2023
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 9004–9017
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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Fact-Checking Complex Claims with Program-Guided Reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 6981–7004
Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, and Preslav Nakov. 2023 · 2023
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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Waddah Saeed and Christian Omlin. 2023 · 2023
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A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making. In Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization . 215–227
Sara Salimzadeh, Gaole He, and Ujwal Gadiraju. 2023 · 2023
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ConvXAI: Delivering heterogeneous AI explanations via conversations to support human-AI scientific writing. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing . 384–387
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Explaining machine learning models with interactive natural language conversations using TalkToModel
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. 2023 · 2023
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Llms as workers in human-computational algorithms? replicating crowdsourcing pipelines with llms
Tongshuang Wu, Haiyi Zhu, Maya Albayrak, Alexis Axon, Amanda Bertsch, Wenxing Deng, Ziqi Ding, Bill Guo, Sireesh Gururaja, Tzu-Sheng Kuo, et al · 2023
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Revisiting out-of-distribution robustness in nlp: Benchmarks, analysis, and LLMs evaluations
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Factuality challenges in the era of large language models and opportunities for fact-checking
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A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 6491–6501
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Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions
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"I’m Not Sure, But…": Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust. In The 2024 ACM Conference on Fairness, Accountability, and Transparency . 822–835
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Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making. In Proceedings of the CHI Conference on Human Factors in Computing Systems . 1–17
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Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
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