Bias does not equal bias: A socio-technical typology of bias in data-based algorithmic systems

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: 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

Classification and Topics

• Classification: Sozialwissenschaften, Soziologie, Anthropologie
• Subject: Artificial intelligence ; Machine learning ; Bias

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lopez, Paola
• Event: Veröffentlichung
• (who): Alexander von Humboldt Institute for Internet and Society
• (where): Berlin
• (when): 2021

Further information

• DOI: doi:10.14763/2021.4.1598
• Handle: http://hdl.handle.net/10419/250397
• Last update: 10.03.2025, 11:44 AM CET