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
Englisch

Erschienen in
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
Sozialwissenschaften, Soziologie, Anthropologie
Thema
Artificial intelligence
Machine learning
Bias

Ereignis
Geistige Schöpfung
(wer)
Lopez, Paola
Ereignis
Veröffentlichung
(wer)
Alexander von Humboldt Institute for Internet and Society
(wo)
Berlin
(wann)
2021

DOI
doi:10.14763/2021.4.1598
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
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

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