Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Price: $159.99 – $102.21
(as of Jan 19,2025 11:37:31 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2021 edition (December 16, 2021)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 333 pages
ISBN-10 ‏ : ‎ 3030833550
ISBN-13 ‏ : ‎ 978-3030833558
Item Weight ‏ : ‎ 1.48 pounds
Dimensions ‏ : ‎ 6.14 x 0.75 x 9.21 inches

Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

Artificial Intelligence (AI) has made significant advancements in recent years, with machine learning algorithms powering everything from recommendation systems to autonomous vehicles. However, one major challenge with traditional AI models is their lack of transparency and interpretability. This has led to concerns about bias, fairness, and accountability in AI systems.

Enter explainable AI, also known as interpretable machine learning. This emerging field focuses on developing AI models that can provide explanations for their decisions and actions. By making AI systems more transparent and understandable, researchers hope to increase trust in AI technologies and enable humans to better understand, interpret, and control these systems.

Explainable AI techniques range from simple rule-based models that are easy to interpret to more complex models that generate explanations for their predictions. These explanations can help users understand why a particular decision was made, identify potential biases in the data, and troubleshoot errors in the model.

In addition to improving transparency and accountability, explainable AI has practical benefits for businesses and organizations. For example, in industries such as healthcare and finance, where decisions have high stakes and legal implications, interpretable machine learning models can help experts validate and trust the predictions made by AI systems.

Overall, explainable AI represents a crucial step towards creating more ethical, fair, and trustworthy AI systems. As researchers continue to develop new techniques and tools for interpretability, the future of AI looks promising, with more transparent and accountable systems that can be understood and controlled by humans.
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