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- W4285245057 abstract "In the recent decade, deep neural networks have contributed to the rapid advance of artificial intelligence (AI). AI became able to learn and predict complex patterns in big data and solve nonlinear problems effectively. However, because deep neural networks were literally too deep, people could only understand some parts of the prediction process. The issue has led people to be concerned about the unknown part of AI systems. Focusing on the limitation, researchers suggested the concept of explainable AI and provided several techniques and policies. This chapter reviews two major issues in AI and how techniques and policies of explainable AI have addressed the issues. Based on the review, I suggest some policy implications and remaining issues together." @default.
- W4285245057 created "2022-07-14" @default.
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- W4285245057 date "2022-01-01" @default.
- W4285245057 modified "2023-09-26" @default.
- W4285245057 title "Designing XAI from policy perspectives" @default.
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- W4285245057 doi "https://doi.org/10.1016/b978-0-323-85648-5.00004-9" @default.
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