Explainable AI
Artificial Intelligence
11 Videos | 46m 27s - Includes Assessment
- Earns a Badge
The inner workings of many deep learning systems are complicated, if not impossible, for the human mind to comprehend. Explainable Artificial Intelligence (XAI) aims to provide AI experts with transparency into these systems. In this course, you'll describe what Explainable AI is, how to use it, and the data structures behind XAI's preferred algorithms. Next, you'll explore the interpretability problem and today's state-of-the-art solutions to it. You'll identify XAI regulations, define the "right to explanation", and illustrate real-world examples where this has been applicable. You'll move on to recognize both the Counterfactual and Axiomatic methods, distinguishing their pros and cons. You'll investigate the intelligible models method, along with the concepts of monotonicity and rationalization. Finally, you'll learn how to use a Generative Adversarial Network.
WHAT YOU WILL LEARN
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discover the key concepts covered in this course
recognize what Explainable AI is and its significance
define the Interpretability Problem and its importance
define 'right to explanation' regulations and identify real-world use cases
identify the meaning and application of the counterfactual method
identify the meaning and application of axiomatic attribution -
define the meaning and recognize the purpose of intelligible models
define the meaning and recognize the real-world use of monotonicity
describe the meaning and application of rationalization
describe the meaning and application of feature visualization
summarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 30sUP NEXT
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2.Explainable AI4m 47s
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3.The Interpretability Problem11m 4s
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4.Right to Explanation2m 38s
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5.Counterfactual Method3m 42s
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6.Axiomatic Attribution1m 58s
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7.Intelligible Models3m 24s
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8.Monotonicity4m 54s
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9.Rationalization2m 2s
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10.Feature Visualization2m 44s
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11.Course Summary2m 14s
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