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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.
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 ...
Regulation of algorithms, rules and laws for algorithms Topics referred to by the same term This disambiguation page lists articles associated with the title Algorithmic regulation .
In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data.Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data.
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 ...
Specifically, "algorithmic transparency" states that the inputs to the algorithm and the algorithm's use itself must be known, but they need not be fair. " Algorithmic accountability " implies that the organizations that use algorithms must be accountable for the decisions made by those algorithms, even though the decisions are being made by a ...
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.