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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.
The development of public sector strategies for management and regulation of AI is deemed necessary at the local, national, [33] and international levels [34] and in a variety of fields, from public service management [35] and accountability [36] to law enforcement, [34] [37] healthcare (especially the concept of a Human Guarantee), [38] [39 ...
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 ...
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
Algorithmic management is a term used to describe certain labor management practices in the contemporary digital economy. In scholarly uses, the term was initially coined in 2015 by Min Kyung Lee, Daniel Kusbit, Evan Metsky, and Laura Dabbish to describe the managerial role played by algorithms on the Uber and Lyft platforms, [1] [2] but has since been taken up by other scholars to describe ...
ModelOps (model operations or model operationalization), as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models" in Multi-Agent Systems. [1] "
Limited risk – AI systems in this category have transparency obligations, ensuring users are informed that they are interacting with an AI system and allowing them to make informed choices. This category includes, for example, AI applications that make it possible to generate or manipulate images, sound, or videos (like deepfakes ).
A typical rule-based system has four basic components: [3] A list of rules or rule base, which is a specific type of knowledge base.; An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base.