Details
| Translated title of the contribution | Organization and Algorithm: How Organizations Make Algorithmic Categories, Comparisons, and Evaluations Relevant |
|---|---|
| Original language | German |
| Pages (from-to) | 333-357 |
| Number of pages | 25 |
| Journal | Kolner Zeitschrift fur Soziologie und Sozialpsychologie |
| Volume | 73 |
| Early online date | 29 Jun 2021 |
| Publication status | Published - Aug 2021 |
Abstract
This article analyzes how organizations endow algorithms, which we understand as digital formats of observation, with agency, thus rendering them actionable. Our main argument is that the relevance of digital observation formats results from how organizations embed them in their decision architectures. We demonstrate this using the example of the Austrian Public Employment Service (AMS), which introduced an algorithm in 2018 to evaluate the chances of unemployed persons being reintegrated in the labor market. In this regard, the AMS algorithm serves as an exemplary case for the current trend among public organizations to harness algorithms for distributing limited resources in a purportedly more efficient way. To reconstruct how this is achieved, we delineate how the AMS algorithm categorizes, compares, and evaluates persons. Building on this, we demonstrate how the algorithmic model is integrated into the organizational decision architecture and thereby made actionable. In conclusion, algorithmic models like the AMS algorithm also pose a challenge for organizations because they mute chances for realizing organizational learning. We substantiate this argument with regard to the role of coproduction and the absence of clear causality in the field of (re)integrating unemployed persons in the labor market.
ASJC Scopus subject areas
- Psychology(all)
- Social Psychology
- Social Sciences(all)
- Sociology and Political Science
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Kolner Zeitschrift fur Soziologie und Sozialpsychologie, Vol. 73, 08.2021, p. 333-357.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Organisation und Algorithmus
T2 - Wie algorithmische Kategorien, Vergleiche und Bewertungen durch Organisationen relevant gemacht werden
AU - Büchner, Stefanie
AU - Dosdall, Henrik
N1 - Funding Information: Der vorliegende Text hat maßgeblich von den instruktiven Anmerkungen der Herausgeberinnen, Bettina Heintz und Theresa Wobbe, profitiert. Dies gilt auch für die kritischen und konstruktiven Diskussionen während des Workshops an der Humboldt Universität für das vorliegende Sonderheft. Zu danken ist ebenfalls Katharina Braunsmann, Korbinian Gall und Justus Rahn sowie den Teilnehmerinnen und Teilnehmern des Kolloquiums am Lehrstuhl für Organisations- und Verwaltungssoziologie der Universität Potsdam für wertvolle Hinweise und Anmerkungen.
PY - 2021/8
Y1 - 2021/8
N2 - This article analyzes how organizations endow algorithms, which we understand as digital formats of observation, with agency, thus rendering them actionable. Our main argument is that the relevance of digital observation formats results from how organizations embed them in their decision architectures. We demonstrate this using the example of the Austrian Public Employment Service (AMS), which introduced an algorithm in 2018 to evaluate the chances of unemployed persons being reintegrated in the labor market. In this regard, the AMS algorithm serves as an exemplary case for the current trend among public organizations to harness algorithms for distributing limited resources in a purportedly more efficient way. To reconstruct how this is achieved, we delineate how the AMS algorithm categorizes, compares, and evaluates persons. Building on this, we demonstrate how the algorithmic model is integrated into the organizational decision architecture and thereby made actionable. In conclusion, algorithmic models like the AMS algorithm also pose a challenge for organizations because they mute chances for realizing organizational learning. We substantiate this argument with regard to the role of coproduction and the absence of clear causality in the field of (re)integrating unemployed persons in the labor market.
AB - This article analyzes how organizations endow algorithms, which we understand as digital formats of observation, with agency, thus rendering them actionable. Our main argument is that the relevance of digital observation formats results from how organizations embed them in their decision architectures. We demonstrate this using the example of the Austrian Public Employment Service (AMS), which introduced an algorithm in 2018 to evaluate the chances of unemployed persons being reintegrated in the labor market. In this regard, the AMS algorithm serves as an exemplary case for the current trend among public organizations to harness algorithms for distributing limited resources in a purportedly more efficient way. To reconstruct how this is achieved, we delineate how the AMS algorithm categorizes, compares, and evaluates persons. Building on this, we demonstrate how the algorithmic model is integrated into the organizational decision architecture and thereby made actionable. In conclusion, algorithmic models like the AMS algorithm also pose a challenge for organizations because they mute chances for realizing organizational learning. We substantiate this argument with regard to the role of coproduction and the absence of clear causality in the field of (re)integrating unemployed persons in the labor market.
KW - Algorithms
KW - Digital observation formats
KW - Digitization
KW - Organizational learning
KW - Public organizations
UR - http://www.scopus.com/inward/record.url?scp=85109028462&partnerID=8YFLogxK
U2 - 10.1007/s11577-021-00752-0
DO - 10.1007/s11577-021-00752-0
M3 - Artikel
AN - SCOPUS:85109028462
VL - 73
SP - 333
EP - 357
JO - Kolner Zeitschrift fur Soziologie und Sozialpsychologie
JF - Kolner Zeitschrift fur Soziologie und Sozialpsychologie
SN - 0023-2653
ER -