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Can I read Explainable AI: To Reveal the Logic of Black-Box Models on EtoBox?
Explainable AI: To Reveal the Logic of Black-Box Models by Chinu; Urvashi Bansal is a Computer Science article available to read on EtoBox.
What is Explainable AI: To Reveal the Logic of Black-Box Models about?
Artificial intelligence (AI) is continuously evolving; however, in the last 10 years, it has gotten considerably more difficult to explain AI models. With the help of explanations, end users can understand the outcomes generated by AI models. The proposed work has shown major issues and gaps in the literature. The main issues found in the literature are unfair/biased decisions made by the model, poor accuracy, reliability, and evaluation metrics to assess the effectiveness of explanations and security of data. Research results obtained in this proposed work highlight the needs, challenges, and opportunities in the field of Explainable artificial intelligence (XAI). How can we make artificial intelligence models explainable? Evaluation of explanations using metrics is the main contribution of this research work. Moreover, the proposed work analyzed different types of explanations, leading companies providing Explainable artificial intelligence services, and open-source tools available in the market for using Explainable artificial intelligence. Finally, based on the reviewed works, the proposed work well-found some future directions for designing more transparent models for artifici
Who reads Explainable AI: To Reveal the Logic of Black-Box Models?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Chinu; Urvashi Bansal
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)