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Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [1][2] The main focus is usually on the reasoning behind the decisions or ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Ethical principles. [edit] In the review of 84 [ 16 ] ethics guidelines for AI, 11 clusters of principles were found: transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, sustainability, dignity, solidarity.
Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. [135] It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. [136]
The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 13 ] [ 14 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...
Trustworthy AI is also a work programme of the International Telecommunication Union, an agency of the United Nations, initiated under its AI for Good programme. [2] Its origin lies with the ITU-WHO Focus Group on Artificial Intelligence for Health, where strong need for privacy at the same time as the need for analytics, created a demand for a standard in these technologies.
Also abduction. A form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. abductive inference, or retroduction abstract data type A mathematical model for data types, where a data type is defined by its ...
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant [1] and others, [2][3] the effective construction of rich computational cognitive models demands ...
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