Transparency in the Wild: Navigating Transparency in a Deployed AI System to Broaden Need-Finding Approaches
Author:
Affiliation:
1. Carnegie Mellon University Software Engineering Institute, United States
2. Carnegie Mellon University, USA
3. Carnegie Mellon University Software Engineering Institute, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3630106.3658985
Reference70 articles.
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3. Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel C. Hoffman Stephanie Houde Q. Vera Liao Ronny Luss Aleksandra Mojsilović Sami Mourad Pablo Pedemonte Ramya Raghavendra John Richards Prasanna Sattigeri Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis Wei and Yunfeng Zhang. 2019. One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. https://arxiv.org/abs/1909.03012v2
4. Tanya Y Berger-Wolf, Daniel I Rubenstein, Charles V Stewart, Jason A Holmberg, Jason Parham, Sreejith Menon, Jonathan Crall, Jon Van Oast, Emre Kiciman, and Lucas Joppa. 2017. Wildbook: Crowdsourcing, computer vision, and data science for conservation. arXiv preprint arXiv:1710.08880 (2017). https://arxiv.org/abs/1710.08880
5. Hugh Beyer and Karen Holtzblatt. 1999. Contextual design. interactions 6, 1 (1999), 32–42. https://dl.acm.org/doi/fullHtml/10.1145/291224.291229
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