A transparência no centro da construção de uma IA ética

Arbix, Glauco

doi:10.25091/s01013300202000020008

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2020

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“…A mechanism is needed to ensure the systematic updating of MLOnto in view of the Never-Ending Learner paradigm [31]. Specialized languages in learning that never-ends, have been proposed for applications in large domains, have been studied but can be thought of applying over restricted and very narrow domains, such as in the SciPy tool facility updates [32] Also, we must consider that the application referred to as Lifelong Machine Learning (LML), considers systems that can learn many tasks from one or more domains over its lifetime [33] It would be appropriate for issues related to ethics in the construction of algorithms and the implementations that associate transparency, law and individual or group values of human society to be incorporated and characterized in MLOnto [34].…”

Section: Future Workmentioning

confidence: 99%

A Machine Learning Ontology

Braga¹,

Dias²,

Regateiro³

2020

Preprint

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This paper describes the creation of an ontology to represent the knowledge around the Machine Learning discipline. Protégé 5 was used, which produces results suitable for agents developed by software and for humans. The knowledge created by Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are displayed and the ontology is made available for public use, in all of its versions.

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Section: Future Workmentioning

confidence: 99%