Predictive privacy: towards an applied ethics of data analytics

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dc.contributor.authorMühlhoff, Rainer
dc.date.accessioned2022-02-10T08:58:52Z
dc.date.available2022-02-10T08:58:52Z
dc.date.issued2021-07-31
dc.description.abstractData analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel - 2021en
dc.description.sponsorshipDFG, 390523135, EXC 2002: Science of Intelligence (SCIoI)en
dc.identifier.eissn1572-8439
dc.identifier.issn1388-1957
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16327
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-15102
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc100 Philosophie und Psychologiede
dc.subject.otherpredictive analyticsen
dc.subject.otherethics of Big Dataen
dc.subject.otherautomated decision makingen
dc.subject.otherbiasen
dc.subject.otherprivacyen
dc.subject.othergroup privacyen
dc.titlePredictive privacy: towards an applied ethics of data analyticsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1007/s10676-021-09606-xen
dcterms.bibliographicCitation.journaltitleEthics and Information Technologyen
dcterms.bibliographicCitation.originalpublishernameSpringer Natureen
dcterms.bibliographicCitation.originalpublisherplaceHeidelbergen
dcterms.bibliographicCitation.pageend690en
dcterms.bibliographicCitation.pagestart675en
dcterms.bibliographicCitation.volume23en
tub.accessrights.dnbfreeen
tub.affiliationFak. 6 Planen Bauen Umwelt::Inst. Landschaftsarchitektur und Umweltplanung::FG Ökonomie des Klimawandelsde
tub.affiliation.facultyFak. 6 Planen Bauen Umweltde
tub.affiliation.groupFG Ökonomie des Klimawandelsde
tub.affiliation.instituteInst. Landschaftsarchitektur und Umweltplanungde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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