Expanding Explainability: Towards Social Transparency in AI systems

12 January 2021

Papers citing "Expanding Explainability: Towards Social Transparency in AI systems"

31 / 31 papers shown

Title
Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest Jakob Schoeffer Maria De-Arteaga Jonathan Elmer 417 0 0 05 Apr 2025
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support Kevin Pu Daniel Lazaro Ian Arawjo Haijun Xia Ziang Xiao Tovi Grossman Yan Chen 115 5 0 25 Feb 2025
Revisiting Rogers' Paradox in the Context of Human-AI Interaction Katherine M. Collins Umang Bhatt Ilia Sucholutsky 127 1 0 16 Jan 2025
Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond Shanshan Han 148 1 0 09 Oct 2024
ShapG: new feature importance method based on the Shapley value Chi Zhao Jing Liu Elena Parilina FAtt 253 4 0 29 Jun 2024
Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability Md. Tanzib Hosain Md. Mehedi Hasan Anik Sadman Rafi̇ Rana Tabassum Khaleque Insi̇a Md. Mehrab Siddiky 40 7 0 13 Oct 2023
Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling Haotian Li Yun Wang Q. V. Liao Huamin Qu 117 23 0 17 Apr 2023
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence Shakir Mohamed Marie-Therese Png William S. Isaac 90 406 0 08 Jul 2020
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs Sungsoo Ray Hong Jessica Hullman E. Bertini HAI 73 194 0 23 Apr 2020
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy B. Shneiderman 58 701 0 10 Feb 2020
Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach Upol Ehsan Mark O. Riedl 55 217 0 04 Feb 2020
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study Ahmed Alqaraawi M. Schuessler Philipp Weiß Enrico Costanza N. Bianchi-Berthouze AAML FAtt XAI 61 200 0 03 Feb 2020
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems Zana Buçinca Phoebe Lin Krzysztof Z. Gajos Elena L. Glassman ELM 73 284 0 22 Jan 2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans Vivian Lai Han Liu Chenhao Tan 78 141 0 14 Jan 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 127 720 0 08 Jan 2020
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making Yunfeng Zhang Q. V. Liao Rachel K. E. Bellamy 83 677 0 07 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 121 6,269 0 22 Oct 2019
What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems Javier Sánchez-Monedero L. Dencik L. Edwards 72 136 0 28 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 67 393 0 06 Sep 2019
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning Thilo Spinner U. Schlegel H. Schäfer Mennatallah El-Assady HAI 65 237 0 29 Jul 2019
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes Qian Yang Aaron Steinfeld John Zimmerman 49 236 0 21 Apr 2019
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions Upol Ehsan Pradyumna Tambwekar Larry Chan Brent Harrison Mark O. Riedl 97 243 0 11 Jan 2019
TED: Teaching AI to Explain its Decisions Michael Hind Dennis L. Wei Murray Campbell Noel Codella Amit Dhurandhar Aleksandra Mojsilović Karthikeyan N. Ramamurthy Kush R. Varshney 56 110 0 12 Nov 2018
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 99 667 0 04 Nov 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 95 1,862 0 31 May 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 102 219 0 20 Mar 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 56 244 0 09 Mar 2018
Manipulating and Measuring Model Interpretability Forough Poursabzi-Sangdeh D. Goldstein Jake M. Hofman Jennifer Wortman Vaughan Hanna M. Wallach 91 698 0 21 Feb 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 247 4,265 0 22 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 402 3,798 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,701 0 10 Jun 2016