Assessing and enforcing fairness in the AI lifecycle

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dc.contributor.authorCalegari, Robertaen
dc.contributor.authorCastañé, Gabriel G.en
dc.contributor.authorMilano, Michelaen
dc.contributor.authorO'Sullivan, Barryen
dc.date.accessioned2023-11-24T16:41:10Z
dc.date.available2023-11-24T16:41:10Z
dc.date.issued2023en
dc.description.abstractA significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCalegari, R., G. Castañé, G., Milano, M. and O’Sullivan, B. (2023) ‘Assessing and enforcing fairness in the AI lifecycle’, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Macau, SAR China: International Joint Conferences on Artificial Intelligence Organization, pp. 6554–6562. https://doi.org/10.24963/ijcai.2023/735.en
dc.identifier.doi10.24963/ijcai.2023/735en
dc.identifier.endpage6562en
dc.identifier.startpage6554en
dc.identifier.urihttps://hdl.handle.net/10468/15259
dc.language.isoenen
dc.publisherIJCAI International Joint Conference on Artificial Intelligenceen
dc.relation.ispartofProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. Macau, SAR China: International Joint Conferences on Artificial Intelligence Organizationen
dc.relation.urihttps://doi.org/10.24963/ijcai.2023/735en
dc.rightsAccepted version © 2023 the authorsen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial intelligence (AI)en
dc.subjectBias mitigationen
dc.subjectFairnessen
dc.subjectAI lifecycleen
dc.subjectExperimentation environmentsen
dc.subjectFairness metricsen
dc.subjectAIen
dc.subjectAI Ethicsen
dc.subjectAI Trusten
dc.subjectAI Fairnessen
dc.subjectMachine Learningen
dc.subjectHumans and AIen
dc.titleAssessing and enforcing fairness in the AI lifecycleen
dc.typeArticle (peer-reviewed)en
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