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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30423
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dc.contributor.authorScantamburlo, Teresa-
dc.contributor.authorBaumann, Joachim-
dc.contributor.authorHeitz, Christoph-
dc.date.accessioned2024-04-06T09:38:57Z-
dc.date.available2024-04-06T09:38:57Z-
dc.date.issued2024-03-16-
dc.identifier.issn0951-5666de_CH
dc.identifier.issn1435-5655de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30423-
dc.description.abstractAn implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofAI & Societyde_CH
dc.rightshttps://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectPrediction-based decision makingde_CH
dc.subjectFair predictionde_CH
dc.subjectAlgorithmic fairnessde_CH
dc.subjectResponsible AIde_CH
dc.subjectHuman-in-the-loopde_CH
dc.subjectPrediction modelerde_CH
dc.subjectResponsibilityde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc170: Ethikde_CH
dc.titleOn prediction-modelers and decision-makers : why fairness requires more than a fair prediction modelde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1007/s00146-024-01886-3de_CH
dc.identifier.doi10.21256/zhaw-30423-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf187473de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.funding.zhawSocially acceptable AI and fairness trade-offs in predictive analyticsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Scantamburlo, T., Baumann, J., & Heitz, C. (2024). On prediction-modelers and decision-makers : why fairness requires more than a fair prediction model. AI & Society. https://doi.org/10.1007/s00146-024-01886-3
Scantamburlo, T., Baumann, J. and Heitz, C. (2024) ‘On prediction-modelers and decision-makers : why fairness requires more than a fair prediction model’, AI & Society [Preprint]. Available at: https://doi.org/10.1007/s00146-024-01886-3.
T. Scantamburlo, J. Baumann, and C. Heitz, “On prediction-modelers and decision-makers : why fairness requires more than a fair prediction model,” AI & Society, Mar. 2024, doi: 10.1007/s00146-024-01886-3.
SCANTAMBURLO, Teresa, Joachim BAUMANN und Christoph HEITZ, 2024. On prediction-modelers and decision-makers : why fairness requires more than a fair prediction model. AI & Society. 16 März 2024. DOI 10.1007/s00146-024-01886-3
Scantamburlo, Teresa, Joachim Baumann, and Christoph Heitz. 2024. “On Prediction-Modelers and Decision-Makers : Why Fairness Requires More than a Fair Prediction Model.” AI & Society, March. https://doi.org/10.1007/s00146-024-01886-3.
Scantamburlo, Teresa, et al. “On Prediction-Modelers and Decision-Makers : Why Fairness Requires More than a Fair Prediction Model.” AI & Society, Mar. 2024, https://doi.org/10.1007/s00146-024-01886-3.


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