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Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. [1] [2] [3] For the subset of AI algorithms, the term regulation of artificial intelligence is used.
Government by algorithm [1] (also known as algorithmic regulation, [2] regulation by algorithms, algorithmic governance, [3] [4] algocratic governance, algorithmic legal order or algocracy [5]) is an alternative form of government or social ordering where the usage of computer algorithms is applied to regulations, law enforcement, and generally any aspect of everyday life such as ...
Regulation of the issues of ethical and legal support for the development of AI is accelerating, and policy ensures state control of Chinese companies and over valuable data, including storage of data on Chinese users within the country and the mandatory use of People's Republic of China's national standards for AI, including over big data ...
It covers all types of AI across a broad range of sectors, with exceptions for AI systems used solely for military, national security, research and non-professional purposes. [5] As a piece of product regulation, it does not confer rights on individuals, but regulates the providers of AI systems and entities using AI in a professional context. [6]
Algorithmic regulation may refer to: Government by algorithm, use of algorithms in government; Regulation of algorithms, rules and laws for algorithms
The design of algorithms is part of many solution theories, such as divide-and-conquer or dynamic programming within operation research. Techniques for designing and implementing algorithm designs are also called algorithm design patterns, [ 38 ] with examples including the template method pattern and the decorator pattern.
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Artificial immune systems (AIS) are a class of rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system.The algorithms are typically modeled after the immune system's characteristics of learning and memory for problem-solving, specifically by the computational techniques called Evolutionary Computation and Amorphous Computation.