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What is explainable AI?
Is explainability in AI the same as interpretability?
What's the need to explain AI models?
What aspects of an AI/ML system need to be explained?
What's the benefit in making AI models explainable?
What are the current techniques in explainable AI?
What are the different frameworks for explainable AI?
What are some limitations of explainable AI?
Makes the article more complete and neutral
Essential but may actually become the Summary
Brief descriptions but could also have dedicated articles; could even delete them here
Dead Code, Naming Conventions, ROC Curve, Byte Ordering, CAP Theorem, Leaky Abstractions, Postel's Law, etc.
Blockchain, 5G New Radio, IoT Security, IEEE 802.11ac, WebRTC, RISC-V Architecture, C++, Python OOP, etc.
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Maybe edited by reviewer based on intimate knowledge of other articles on the system
I didn’t have time to write a short letter, so I wrote a long one instead.
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Papers usually refer to other important papers in the field. Follow these links to research the topic at a deeper level.
Adopted from Wikipedia
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Image explains a concept
6 + 14 + 8 + 17 = 45 words
gRPC is a framework that enables the implementation of highly scalable and performant application whose parts are distributed over a network. The framework abstracts away the low-level networking details from app developers so that they can focus on the application logic. Developers need not worry about how one part calls a functionality in another remote part. gRPC takes care of this and enables more responsive real-time apps.
The two terms are used interchangeably although it's worth understanding the difference. A model is interpretable if ... A model is explainable if ...
However, there's no precise or universal definition. Rudin says interpretable models are ... Lipton claims that interpretability asks "How does the model work?" whereas explainability asks "What else can the model tell me?"
Montavon et al. talk about interpretability in terms of human understanding: we can interpret ... Explainability takes interpretable features to show ...
L.B. Lusted applied ROC methodology to compare different studies of chest film interpretations for detection of pulmonary tuberculosis. This was the first application of ROC to radiology. It inspired the use of ROC in many diagnostic imaging systems. Lusted himself published Decision-making studies in patient management in 1971.
L.B. Lusted applies ROC methodology to compare different studies of chest film interpretations for detection of pulmonary tuberculosis. This is the first application of ROC to radiology. It subsequently inspires the use of ROC in many diagnostic imaging systems. Lusted himself publishes Decision-making studies in patient management in 1971.
Adapted from Chicago Manual of Style (Author-Date system)
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