Fetching the paper…
Reading the bibliography…
Large language models can be prompted to produce text.
The mindlessness of ostensibly thoughtful action: The role of" placebic" information in interpersonal interaction
Ellen J Langer, Arthur Blank, and Benzion Chanowitz. 1978 · 1978
Earlier work this paper cites.
AI in reverse: Computer tools that turn cognitive
Gavriel Salomon. 1988 · 1988
Earlier work this paper cites.
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
Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong. 2013 · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Earlier work this paper cites.
Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13
Matthew D Zeiler and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th international conference on intelligent user interfaces
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015 · 2015
Earlier work this paper cites.
Interactive visual machine learning in spreadsheets. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
Advait Sarkar, Mateja Jamnik, Alan F. Blackwell, and Martin Spott. 2015 · 2015
Earlier work this paper cites.
"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
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Interactive analytical modelling
Advait Sarkar. 2016 · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
The Impact of Placebic Explanations on Trust in Intelligent Systems. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
Malin Eiband, Daniel Buschek, Alexander Kremer, and Heinrich Hussmann. 2019 · 2019
Earlier work this paper cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
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
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, and others. 2021 · 2021
Cited alongside, same era.
Expanding explainability: Towards social transparency in ai systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Upol Ehsan, Q Vera Liao, Michael Muller, Mark O Riedl, and Justin D Weisz. 2021a · 2021
Cited alongside, same era.
The who in explainable ai: How ai background shapes perceptions of ai explanations
Upol Ehsan, Samir Passi, Q Vera Liao, Larry Chan, I Lee, Michael Muller, Mark O Riedl, and others. 2021b · 2021
Cited alongside, same era.
Can large language models explain themselves? a study of llm-generated self-explanations
Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, and Leilani H Gilpin. 2023 · 2023
Later among the works it cites.
Fostering Youth’s Critical Thinking Competency About AI through Exhibition. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Sunok Lee, Dasom Choi, Minha Lee, Jonghak Choi, and Sangsu Lee. 2023 · 2023
Later among the works it cites.
“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Benjamin Zorn, Jack Williams, Neil Toronto, and Andrew D Gordon. 2023 · 2023
Later among the works it cites.
New York lawyers sanctioned for using fake ChatGPT cases in legal brief
Sara Merken. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Social Construction of XAI: Do We Need One Definition to Rule Them All?
Upol Ehsan and Mark O. Riedl. 2022 · 2022
Cited alongside, same era.
Is explainable AI a race against model complexity?. In Workshop on Transparency and Explanations in Smart Systems (TeXSS), in conjunction with ACM Intelligent User Interfaces (IUI 2022)
Advait Sarkar. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and others. 2022 · 2022
Cited alongside, same era.
Deceptive patterns – user interfaces designed to trick you
H Brignull, M Leiser, C Santos, and K Doshi. 2023 · 2023
Cited alongside, same era.
Lawyer cited 6 fake cases made up by CHATGPT; judge calls it “unprecedented”
Jon Brodkin. 2023 · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, and others. 2023 · 2023
Cited alongside, same era.
Don’t Just Tell Me, Ask Me: AI Systems that Intelligently Frame Explanations as Questions Improve Human Logical Discernment Accuracy over Causal AI explanations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Valdemar Danry, Pat Pataranutaporn, Yaoli Mao, and Pattie Maes. 2023 · 2023
Cited alongside, same era.
Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
Upol Ehsan, Koustuv Saha, Munmun De Choudhury, and Mark O Riedl. 2023 · 2023
Cited alongside, same era.
Jiachen Zhao, Zonghai Yao, Zhichao Yang, and Hong Yu. 2023 · 2023
Later among the works it cites.
In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making
Raymond Fok and Daniel S. Weld. 2024 · 2024
Closest in time.
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao and Jennifer Wortman Vaughan. 2024 · 2024
Closest in time.
Are self-explanations from Large Language Models faithful?
Andreas Madsen, Sarath Chandar, and Siva Reddy. 2024 · 2024
Closest in time.
AI Should Challenge, Not Obey
Advait Sarkar. 2024 · 2024
Closest in time.
Language Models Struggle to Explain Themselves
Dane Sherburn, Bilal Chughtai, and Owain Evans. 2024 · 2024
Closest in time.
Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting
Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. 2024 · 2024
Closest in time.
Explainability for large language models: A survey
Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, and Mengnan Du. 2024 · 2024
Closest in time.