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ASIN ‏ : ‎ B09NPPRBJ6
Publisher ‏ : ‎ Springer (December 15, 2021)
Publication date ‏ : ‎ December 15, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 60053 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 497 pages

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Artificial Intelligence (AI) has made significant advancements in recent years, with machine learning algorithms being used in a wide range of applications such as healthcare, finance, and autonomous vehicles. However, one of the main challenges of AI is the lack of transparency and interpretability in the decision-making process of these algorithms.

Explainable Artificial Intelligence, also known as Interpretable Machine Learning, aims to address this issue by providing insights into how AI models arrive at their predictions or decisions. This is crucial for ensuring accountability, trust, and understanding of AI systems, especially in high-stakes scenarios where human lives or critical decisions are involved.

Interpretable Machine Learning involves techniques that make AI models more transparent and easier to understand for humans. This includes methods such as feature importance analysis, model visualization, and rule-based explanations that provide insights into the inner workings of the AI model.

By incorporating explainability into AI systems, researchers and practitioners can ensure that the decisions made by AI models are not only accurate but also ethically sound and aligned with human values. This is particularly important as AI becomes increasingly integrated into our daily lives and decision-making processes.

In this post, we will explore the concept of Explainable Artificial Intelligence in more detail, discussing the importance of interpretability in AI systems and the various techniques and tools that can be used to make machine learning models more transparent and understandable. Stay tuned for more insights on how Interpretable Machine Learning is shaping the future of AI.
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