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arxiv
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
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In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models.
48952019年10月22日发表计算机科学 Information Fusion
Science
Experimental evidence on the productivity effects of generative artificial intelligence
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The assistive chatbot ChatGPT raises productivity in professional writing tasks and reduces productivity inequality, which carries policy implications for efforts to reduce productivity inequality through AI.
3002023年07月14日发表计算机科学Science
SSRN
Foundations of Machine Learning
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The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance. The machine learning paradigm can be viewed as "programming by example." Often we have a specific task in mind, such as spam filtering. But rather than program the computer to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that i provide.
52019年06月17日发表计算机科学Patna University
nature
Stable learning establishes some common ground between causal inference and machine learning
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The fundamental problems addressed by stable learning are identified, as well as the latest progress from both causal inference and learning perspectives, and the relationships with explainability and fairness problems are discussed.
1092022年02月01日发表机器智能Nature Machine Intelligence