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The motivation for regulation of algorithms is the apprehension of losing control over the algorithms, whose impact on human life increases. Multiple countries have already introduced regulations in case of automated credit score calculation— right to explanation is mandatory for those algorithms.
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 is now generally considered necessary to both encourage AI and manage associated risks. [19] [20] [21] Public administration and policy considerations generally focus on the technical and economic implications and on trustworthy and human-centered AI systems, [22] although regulation of artificial superintelligences is also ...
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.
Flowchart of using successive subtractions to find the greatest common divisor of number r and s. In mathematics and computer science, an algorithm (/ ˈ æ l ɡ ə r ɪ ð əm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [1]
Algorithmic regulation may refer to: Government by algorithm, use of algorithms in government; Regulation of algorithms, rules and laws for algorithms
Cybernetics is often understood within the context of systems science, systems theory, and systems thinking. [ 46 ] [ 47 ] Systems approaches influenced by cybernetics include critical systems thinking , which incorporates the viable system model ; systemic design ; and system dynamics , which is based on the concept of causal feedback loops.
Rule-based machine learning approaches include learning classifier systems, [4] association rule learning, [5] artificial immune systems, [6] and any other method that relies on a set of rules, each covering contextual knowledge.