AI Tools in Algorithmic Management: Early Insights from Germany and Challenges for a fair and co-determined Design

Martin Brenzke, Thea Radüntz
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:313-317, 2026.

Abstract

The increasing use of artificial intelligence (AI)-based systems in the workplace and their consequences on workers’ well-being is a central topic in the current surge of AI. Especially in the field of algorithmic management (AM), which involves the managing of workers based on or supported by algorithms. In this field the information and power imbalance between employees and employers can have a significant influence on the conditions of work. Moreover, it is often unclear what systems are actually being deployed, to what extent these include AI, and on what type of workers’ data these systems operate. These questions are of utmost relevance to ensure a fair and co-determined design of AM processes. In our contribution, we report early insights from an ongoing qualitative interview study conducted in the German labor market. We carried out semi-structured expert interviews with three works council representatives from companies in the electrical industry, IT, and healthcare sectors. Our preliminary findings indicate that AI-based AM is currently mainly used in less critical areas of personnel management, while planned expansions into more sensitive domains are already emerging. At the same time, challenges related to transparency, data governance, and co-determination are already becoming visible in practice. These results underline the importance of a socio-technical perspective on AI in AM and highlight the need for further research on both the technical design and governance structures of such systems to support fair and employee-centered implementation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-brenzke26a, title = {AI Tools in Algorithmic Management: Early Insights from Germany and Challenges for a fair and co-determined Design}, author = {Brenzke, Martin and Rad\"untz, Thea}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {313--317}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/brenzke26a/brenzke26a.pdf}, url = {https://proceedings.mlr.press/v350/brenzke26a.html}, abstract = {The increasing use of artificial intelligence (AI)-based systems in the workplace and their consequences on workers’ well-being is a central topic in the current surge of AI. Especially in the field of algorithmic management (AM), which involves the managing of workers based on or supported by algorithms. In this field the information and power imbalance between employees and employers can have a significant influence on the conditions of work. Moreover, it is often unclear what systems are actually being deployed, to what extent these include AI, and on what type of workers’ data these systems operate. These questions are of utmost relevance to ensure a fair and co-determined design of AM processes. In our contribution, we report early insights from an ongoing qualitative interview study conducted in the German labor market. We carried out semi-structured expert interviews with three works council representatives from companies in the electrical industry, IT, and healthcare sectors. Our preliminary findings indicate that AI-based AM is currently mainly used in less critical areas of personnel management, while planned expansions into more sensitive domains are already emerging. At the same time, challenges related to transparency, data governance, and co-determination are already becoming visible in practice. These results underline the importance of a socio-technical perspective on AI in AM and highlight the need for further research on both the technical design and governance structures of such systems to support fair and employee-centered implementation.} }
Endnote
%0 Conference Paper %T AI Tools in Algorithmic Management: Early Insights from Germany and Challenges for a fair and co-determined Design %A Martin Brenzke %A Thea Radüntz %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-brenzke26a %I PMLR %P 313--317 %U https://proceedings.mlr.press/v350/brenzke26a.html %V 350 %X The increasing use of artificial intelligence (AI)-based systems in the workplace and their consequences on workers’ well-being is a central topic in the current surge of AI. Especially in the field of algorithmic management (AM), which involves the managing of workers based on or supported by algorithms. In this field the information and power imbalance between employees and employers can have a significant influence on the conditions of work. Moreover, it is often unclear what systems are actually being deployed, to what extent these include AI, and on what type of workers’ data these systems operate. These questions are of utmost relevance to ensure a fair and co-determined design of AM processes. In our contribution, we report early insights from an ongoing qualitative interview study conducted in the German labor market. We carried out semi-structured expert interviews with three works council representatives from companies in the electrical industry, IT, and healthcare sectors. Our preliminary findings indicate that AI-based AM is currently mainly used in less critical areas of personnel management, while planned expansions into more sensitive domains are already emerging. At the same time, challenges related to transparency, data governance, and co-determination are already becoming visible in practice. These results underline the importance of a socio-technical perspective on AI in AM and highlight the need for further research on both the technical design and governance structures of such systems to support fair and employee-centered implementation.
APA
Brenzke, M. & Radüntz, T.. (2026). AI Tools in Algorithmic Management: Early Insights from Germany and Challenges for a fair and co-determined Design. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:313-317 Available from https://proceedings.mlr.press/v350/brenzke26a.html.

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