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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.
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 ...
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 ...
Algorithm engineering focuses on the design, analysis, implementation, optimization, profiling and experimental evaluation of computer algorithms, bridging the gap between algorithmics theory and practical applications of algorithms in software engineering. [1] It is a general methodology for algorithmic research. [2]
Design science research (DSR) is a research paradigm focusing on the development and validation of prescriptive knowledge in information science. Herbert Simon distinguished the natural sciences, concerned with explaining how things are, from design sciences which are concerned with how things ought to be, [1] that is, with devising artifacts to attain goals.
Modern cryptography is heavily based on mathematical theory and computer science practice; cryptographic algorithms are designed around computational hardness assumptions, making such algorithms hard to break in practice by any adversary. It is theoretically possible to break such a system, but it is infeasible to do so by any known practical ...
In such cases, one particular methodological theory is rejected but not methodology at large when understood as a field of research comprising many different theories. [ 14 ] [ 10 ] In this regard, many objections to methodology focus on the quantitative approach, specifically when it is treated as the only viable approach.
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.