Explainable AI with Python

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(as of Dec 24,2024 05:52:43 UTC – Details)

ASIN ‏ : ‎ B093S1PMWR
Publisher ‏ : ‎ Springer; 1st ed. 2021 edition (April 28, 2021)
Publication date ‏ : ‎ April 28, 2021
Language ‏ : ‎ English
File size ‏ : ‎ 30393 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 325 pages

Explainable AI with Python: Understanding the Black Box

Artificial Intelligence (AI) has become an integral part of our daily lives, from recommendation systems on e-commerce websites to self-driving cars. However, one of the biggest challenges with AI is the lack of transparency in how decisions are made. This is where Explainable AI (XAI) comes in.

Explainable AI refers to the ability to understand and interpret the decisions made by AI systems. By providing explanations for how a model arrived at a particular decision, XAI can help improve trust, accountability, and transparency in AI systems.

Python, a popular programming language for machine learning and AI, offers several tools and libraries that can be used to implement XAI techniques. One such library is the ‘shap’ library, which stands for SHapley Additive exPlanations. ‘shap’ provides a unified approach to explain the output of any machine learning model, including complex models such as deep neural networks.

Another popular library for XAI in Python is ‘lime’ (Local Interpretable Model-agnostic Explanations), which provides local explanations for individual predictions made by a model. By generating simple, interpretable explanations, ‘lime’ can help users understand the reasoning behind AI decisions.

Overall, implementing Explainable AI with Python can help improve the trustworthiness and reliability of AI systems. By understanding the inner workings of AI models, we can ensure that these systems are making decisions that align with our values and expectations.
#Explainable #Python