From risk mitigation to employee action along the Machine Learning Pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace
A. Mollen, and L. Hondrich. Forschungsförderung Working Paper, 278. Hans-Böckler-Stiftung, Düsseldorf, (March 2023)
Abstract
Automated decision-making (ADM) systems in the workplace aggravate the power imbalance between employees and employers by making potentially crucial decisions about employees. Current approaches focus on risk mitigation to safeguard employee interests. While limiting risks remains important, employee representatives should be able to include their interests in the decision-making of ADM systems. This paper introduces the concept of the Machine Learning Pipeline to demonstrate how these interests can be implemented in practice and point to necessary structural transformations.
%0 Report
%1 mollen2023mitigation
%A Mollen, Anne
%A Hondrich, Lukas
%C Düsseldorf
%D 2023
%K Artificial_Intelligence adm automatic_decision_making hbs-2021-297-2 human_resource_management risk-based_approach risk_mitigation workplace workplace_democracy
%N 278
%T From risk mitigation to employee action along the Machine Learning Pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace
%U https://www.boeckler.de/de/faust-detail.htm?produkt=HBS-008565
%X Automated decision-making (ADM) systems in the workplace aggravate the power imbalance between employees and employers by making potentially crucial decisions about employees. Current approaches focus on risk mitigation to safeguard employee interests. While limiting risks remains important, employee representatives should be able to include their interests in the decision-making of ADM systems. This paper introduces the concept of the Machine Learning Pipeline to demonstrate how these interests can be implemented in practice and point to necessary structural transformations.
@techreport{mollen2023mitigation,
abstract = {Automated decision-making (ADM) systems in the workplace aggravate the power imbalance between employees and employers by making potentially crucial decisions about employees. Current approaches focus on risk mitigation to safeguard employee interests. While limiting risks remains important, employee representatives should be able to include their interests in the decision-making of ADM systems. This paper introduces the concept of the Machine Learning Pipeline to demonstrate how these interests can be implemented in practice and point to necessary structural transformations.},
added-at = {2023-03-16T20:36:58.000+0100},
address = {Düsseldorf},
author = {Mollen, Anne and Hondrich, Lukas},
biburl = {https://www.bibsonomy.org/bibtex/24b670842752821dfb28ba6afca31e79c/meneteqel},
institution = {Hans-Böckler-Stiftung},
interhash = {fad65d9e13df3883bfd8ee3fe3fc1bb9},
intrahash = {4b670842752821dfb28ba6afca31e79c},
keywords = {Artificial_Intelligence adm automatic_decision_making hbs-2021-297-2 human_resource_management risk-based_approach risk_mitigation workplace workplace_democracy},
language = {de-DE},
month = mar,
number = 278,
timestamp = {2023-08-31T12:14:44.000+0200},
title = {From risk mitigation to employee action along the Machine Learning Pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace},
type = {Forschungsförderung Working Paper},
url = {https://www.boeckler.de/de/faust-detail.htm?produkt=HBS-008565},
year = 2023
}