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Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them.
Three forms of interpretative flexibility
Uli Meyer and Ingo Schulz-Schaeffer. 2006 · 2006
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
Earlier work this paper cites.
Stakeholders in explainable AI
Alun Preece, Dan Harborne, Dave Braines, Richard Tomsett, and Supriyo Chakraborty. 2018 · 2018
Earlier work this paper cites.
Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Earlier work this paper cites.
The platform as factory: Crowdwork and the hidden labour behind artificial intelligence
Moritz Altenried. 2020 · 2020
Earlier work this paper cites.
Explainable machine learning in deployment. In Proceedings of the 2020 conference on fairness, accountability, and transparency
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley. 2020 · 2020
Earlier work this paper cites.
What Do People Really Want When They Say They Want" Explainable AI?" We Asked 60 Stakeholders.. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
Andrea Brennen. 2020 · 2020
Earlier work this paper cites.
A methodology for creating AI FactSheets
John Richards, David Piorkowski, Michael Hind, Stephanie Houde, and Aleksandra Mojsilović. 2020 · 2020
Earlier work this paper cites.
Explainability fact sheets: A framework for systematic assessment of explainable approaches. In Proceedings of the 2020 conference on fairness, accountability, and transparency
Kacper Sokol and Peter Flach. 2020 · 2020
Earlier work this paper cites.
Data-centric explanations: explaining training data of machine learning systems to promote transparency. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Ariful Islam Anik and Andrea Bunt. 2021 · 2021
Cited alongside, same era.
Who needs to know what, when?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle. In Designing Interactive Systems Conference 2021
Shipi Dhanorkar, Christine T Wolf, Kun Qian, Anbang Xu, Lucian Popa, and Yunyao Li. 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.
How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Agathe Balayn, Natasa Rikalo, Christoph Lofi, Jie Yang, and Alessandro Bozzon. 2022 · 2022
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Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy K Heger, Liz B Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan. 2022 · 2022
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Evaluating a methodology for increasing AI transparency: A case study
David Piorkowski, John Richards, and Michael Hind. 2022 · 2022
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The foundation model transparency index
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, and Percy Liang. 2023 · 2023
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
AI explainability: Why one explanation cannot fit all. In ACM CHI Workshop on Operationalizing Human-Centered Perspectives in Explainable AI (HCXAI)
Milda Norkute. 2021 · 2021
Cited alongside, same era.
Data and its (dis) contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M Bender, Emily Denton, and Alex Hanna. 2021 · 2021
Cited alongside, same era.
Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Harini Suresh, Steven R Gomez, Kevin K Nam, and Arvind Satyanarayan. 2021 · 2021
Cited alongside, same era.
Understanding accountability in algorithmic supply chains. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency
Jennifer Cobbe, Michael Veale, and Jatinder Singh. 2023 · 2023
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Adaptation of AI Explanations to Users’ Roles. In Workshop on Human-Centered Explainable AI (@ CHI 2023)
Julien Delaunay, Christine Largouët, Luis Galárraga, and Niels Van Berkel. 2023 · 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
Sunnie SY Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, and Andrés Monroy-Hernández. 2023 · 2023
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Dislocated accountabilities in the “AI supply chain”: Modularity and developers’ notions of responsibility
David Gray Widder and Dawn Nafus. 2023 · 2023
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Mireia Yurrita, Agathe Balayn, and Ujwal Gadiraju. 2023 · 2023
Later among the works it cites.