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Implications of causality in artificial intelligence

dc.contributor.authorCavique, Luís
dc.date.accessioned2025-10-13T14:51:31Z
dc.date.available2025-10-13T14:51:31Z
dc.date.issued2024-08-21
dc.description.abstractOver the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the ‘black box’ and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-andeffect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.eng
dc.identifier.doi10.3389/frai.2024.1439702
dc.identifier.urihttp://hdl.handle.net/10400.2/20348
dc.language.isoeng
dc.peerreviewedyes
dc.publisherCavique L (2024) Implications of causality in artificial intelligence. Front. Artif. Intell. 7:1439702. doi: 10.3389/frai.2024.1439702
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAI bias
dc.subjectResponsible AI
dc.subjectFair AI
dc.subjectExplainable AI
dc.subjectCausal AI
dc.titleImplications of causality in artificial intelligenceeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleFrontiers in Artificial Intelligence
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCavique
person.givenNameLuís
person.identifier.ciencia-id911E-84AC-3956
person.identifier.orcid0000-0002-5590-1493
relation.isAuthorOfPublication40906a16-46a2-42f1-b26d-7db7012294ee
relation.isAuthorOfPublication.latestForDiscovery40906a16-46a2-42f1-b26d-7db7012294ee

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