Who needs to know what, when?: Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle
Author:
Affiliation:
1. Pennsylvania State University, United States
2. Independent Researcher, United States
3. Amazon.com, Inc., United States
4. IBM Research - Almaden, United States
5. Almaden Research Lab IBM Research, United States
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3461778.3462131
Reference57 articles.
1. Trends and Trajectories for Explainable, Accountable and Intelligible Systems
2. COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
3. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
4. Interpreting Neural Networks to Improve Politeness Comprehension
5. The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms
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