To address this, the field of explainable artificial intelligence, or XAI for short, has emerged, seeking to provide means by which AI-based decisions can be explained to experts, users, or other stakeholders. While it is commonly claimed that explanations of artificial intelligence (AI) ...
论文:《Explanation in artificial intelligence: Insights from the social sciences》 Abstract 1. Introduction 1.1 Scope 1.2 Major findings 1.3 Outline 1.4 Example 论文:《Explanation in artificial intelligence: Insights from the social sciences》 看的时候随手记的笔记,有不理解的地方可以提出一起讨论一下呀!
Journal 2019, Artificial IntelligenceTim Miller Chapter Explanation-based Learning, Cognitive Psychology of See also: Cognitive Theory: ACT; Cognitive Theory: SOAR; Deductive Reasoning Systems; Discovery Learning, Cognitive Psychology of; Explanation, Cognitive Psychology of; Problem Solving and Reasoning: ...
AI, or artificial intelligence, is broadly defined as machine systems that aim to simulate human intelligence. It's not one technology, but rather an umbrella term. Machine learning, or ML, is a subset of AI. ML algorithms independently learn from and detect patterns in data, without being e...
Vision Guided Robotics & Artificial Intelligence: An Explanation for the Non-Technical The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as “artificial intelligence” to convey the idea that solutions are more capa...
Sifting through job applications, analyzing X-ray images, suggesting a new track list—interaction between humans and machines has become an integral part of modern life. The basis for these artificial intelligence (AI) ...
Explainable artificial intelligence (XAI) is gradually becoming a key component of many artificial intelligence systems. However, such pursuit of transparency may bring potential privacy threats to the model confidentially, as the adversary may obtain more critical information about the model. In this pa...
is specifically mentioned in the European Union's General Data Protection Regulation." The goal of making algorithms accessible is central to what is known as "eXplainable Artificial Intelligence (XAI)": "In explainability research, the focus is currently on the desired outcomes of transparency and ...
Rich, E. (1983)Artificial Intelligence, McGraw-Hill, New York. Google Scholar Rosenbloom, P. S. and Laird, J. E. (1986) Mapping explanation-based generalization onto Soar,Proceedings of The National Conference On Artificial Intelligence '86, 1. ...
, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (pp. 498-507). Corvallis, OR: AUAI Press.Pacer et al., 2013] M. Pacer, T. Lombrozo, T. Griths, J. Williams, and X. Chen: Evaluating computational models of explanation using human judgments. UAI, pages 498...