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Causality: the next step in artificial intelligence

dc.contributor.authorCavique, Luís
dc.contributor.editorMoutinho , Luiz
dc.contributor.editorCavique , Luís
dc.contributor.editorBigné, Enrique
dc.date.accessioned2025-10-13T16:09:46Z
dc.date.available2025-10-13T16:09:46Z
dc.date.issued2023-10-18
dc.description.abstractJudea Pearl’s ladder of causation framework has dramatically influenced the understanding of causality in computer science. Despite artificial intelligence (AI) advancements, grasping causal relationships remains challenging, emphasizing the causal revolution’s significance in improving AI’s understanding of cause and effect. The work presents a novel taxonomy of causal inference methods, clarifying diverse approaches for inferring causality from data. It highlights the implications of causality in responsible AI and explainable AI (xAI), addressing bias in AI systems. The chapter points out causality as the next step in AI for creating new questions, developing causal tools, and clarifying opaque models with xAI approaches. The work clarifies causal models’ significance and implications in various AI subareas.por
dc.identifier.doi10.4018/978-1-6684-9591-9.ch001
dc.identifier.isbn9781668495919
dc.identifier.urihttp://hdl.handle.net/10400.2/20350
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIGI-Global
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleCausality: the next step in artificial intelligencepor
dc.typebook part
dspace.entity.typePublication
oaire.citation.titlePhilosophy of artificial intelligence and its place in society
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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