Artikel
Bias does not equal bias: A socio-technical typology of bias in data-based algorithmic systems
This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. The typology is linked to the conceptualisations of legal anti-discrimination regulations, so that the concept of structural inequality-and, therefore, of undesirable bias-is defined accordingly. By analysing the controversial Austrian "AMS algorithm" as a case study as well as examples in the contexts of face detection, risk assessment and health care management, this paper defines the following three types of bias: firstly, purely technical bias as a systematic deviation of the datafied version of a phenomenon from reality; secondly, socio-technical bias as a systematic deviation due to structural inequalities, which must be strictly distinguished from, thirdly, societal bias, which depicts-correctly-the structural inequalities that prevail in society. This paper argues that a clear distinction must be made between different concepts of bias in such systems in order to analytically assess these systems and, subsequently, inform political action.
- Sprache
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Englisch
- Erschienen in
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Journal: Internet Policy Review ; ISSN: 2197-6775 ; Volume: 10 ; Year: 2021 ; Issue: 4 ; Pages: 1-29 ; Berlin: Alexander von Humboldt Institute for Internet and Society
- Klassifikation
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Sozialwissenschaften, Soziologie, Anthropologie
- Thema
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Artificial intelligence
Machine learning
Bias
- Ereignis
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Geistige Schöpfung
- (wer)
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Lopez, Paola
- Ereignis
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Veröffentlichung
- (wer)
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Alexander von Humboldt Institute for Internet and Society
- (wo)
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Berlin
- (wann)
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2021
- DOI
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doi:10.14763/2021.4.1598
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Lopez, Paola
- Alexander von Humboldt Institute for Internet and Society
Entstanden
- 2021