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The Importance of Interpretability in AI Systems and Its Implications for Deep Learning: Ensuring Transparency in Intelligent Systems

The Importance of Interpretability in AI Systems and Its Implications for Deep Learning: Ensuring Transparency in Intelligent Systems

Muhammad Adnan
Copyright: © 2024 |Pages: 36
ISBN13: 9798369317389|ISBN13 Softcover: 9798369345689|EISBN13: 9798369317396
DOI: 10.4018/979-8-3693-1738-9.ch003
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MLA

Adnan, Muhammad. "The Importance of Interpretability in AI Systems and Its Implications for Deep Learning: Ensuring Transparency in Intelligent Systems." Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems, edited by M. Irfan Uddin and Wali Khan Mashwani, IGI Global, 2024, pp. 41-76. https://doi.org/10.4018/979-8-3693-1738-9.ch003

APA

Adnan, M. (2024). The Importance of Interpretability in AI Systems and Its Implications for Deep Learning: Ensuring Transparency in Intelligent Systems. In M. Uddin & W. Mashwani (Eds.), Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems (pp. 41-76). IGI Global. https://doi.org/10.4018/979-8-3693-1738-9.ch003

Chicago

Adnan, Muhammad. "The Importance of Interpretability in AI Systems and Its Implications for Deep Learning: Ensuring Transparency in Intelligent Systems." In Deep Learning, Reinforcement Learning, and the Rise of Intelligent Systems, edited by M. Irfan Uddin and Wali Khan Mashwani, 41-76. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1738-9.ch003

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Abstract

Particularly inside the context of deep learning, the concept of interpretability in artificial intelligence systems is crucial for boosting the degree of trust and self-belief that human beings have in machine-learning fashions. Deep learning models have many parameters and complex architectures that make them function like mysterious “black boxes,” making it difficult for users to apprehend how they function. This opacity increases questions about those models' ethics, dependability, and viable biases. In the field of deep learning, achieving interpretability is crucial for several reasons. First off, interpretable models enhance transparency by making the model's judgments and forecasts simpler for customers to understand. This is particularly essential in complicated fields like banking and healthcare, wherein knowledge and self-assurance are vital. Moreover, interpretability facilitates the identification and correction of biases in the model or the training statistics, performing as a car for fairness and duty.

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