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Principles of Explainable Artificial Intelligence
2021The last decade has witnessed the rise of a black box society where obscure classification models are adopted by Artificial Intelligence systems (AI). The lack of explanations of how AI systems make decisions is a key ethical issue to their adoption in socially sensitive and safety-critical contexts.
Guidotti, R. +3 more
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Towards explainable artificial intelligence
2022In dieser Arbeit entwickeln wir ein Framework für die Interpretation der Entscheidungen hochgradig nichtlinearer Klassifizierungsfunktionen mit Schwerpunkt auf neuronalen Netzen. Insbesondere formalisieren wir die Idee der Trennung der Eingabeparameter in relevante und irrelevante Parameter als explizites Optimierungsproblem.
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Artificial intelligence for natural product drug discovery
Nature Reviews Drug Discovery, 2023Michael W Mullowney +2 more
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Explainable Artificial Intelligence (XAI)
Explainable AI (XAI) is important in situations where decisions have significant effects on the results to make the AI systems more reliable, transparent, and make people understand how AI systems work. In this chapter, an overview of AI, its evolution are discussed, emphasizing the need for robust policy and regulatory frameworks for responsible AI ...+4 more sources
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
New England Journal of Medicine, 2023Charlotte Haug
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Artificial intelligence in cancer target identification and drug discovery
Signal Transduction and Targeted Therapy, 2022Xin Lai, Yi Pan, Julio Vera
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A Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications
IEEE Communications Surveys and Tutorials, 2023Yiping Zuo, Jiajia Guo, Gao Ning
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Artificial Intelligence Applied to Battery Research: Hype or Reality?
Chemical Reviews, 2022Teo Lombardo +2 more
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Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Progress in Materials Science, 2023Jian Lin
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