May 1, 2024
3 minute read
Transparency in AI, also known as Explainable AI or XAI, is the ability to understand and explain the decisions made by AI algorithms. It involves making the inner workings of AI models accessible and understandable to humans. This is important for building trust in AI systems, as it allows users to understand why AI makes certain decisions and how it arrives at those conclusions.
Why Learn Transparency in AI?
There are several reasons why one might want to learn about transparency in AI:
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Find a path to becoming a Transparency in AI. Learn more at:
OpenCourser.com/topic/54fjm1/transparency
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Transparency in AI.
A comprehensive overview of transparent and interpretable machine learning, covering topics such as model interpretability, feature selection, and model simplification.
Provides a holistic overview of human-centered AI, covering topics such as transparency, interpretability, fairness, and accountability in AI systems.
A practical guide to interpretable machine learning, focusing on techniques for understanding and explaining the predictions of black-box models.
Although this book does not specifically address AI transparency, it provides a comprehensive overview of responsible AI, including topics such as transparency, accountability, and fairness in AI systems.
An introduction to the ethical and legal issues surrounding AI, including topics such as transparency, accountability, and bias in AI systems.
Although this book does not specifically address AI transparency, it provides a broader perspective on the societal implications of transparency in technology and how it relates to privacy and freedom.
Although this book does not specifically address AI transparency, it provides a comprehensive overview of the ethical issues surrounding AI, including topics such as transparency, accountability, and fairness.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/54fjm1/transparency
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