Project

Algorithmic management at work - ALGOSH

Algorithmic management is changing management practices and the organization of work. Digital systems and AI-driven solutions are increasingly involved in the planning, monitoring and evaluation of work. These systems shape employees’ experiences, job demands and resources, and opportunities for influence at work. Our project is part of the international ALGOSH research programme, through which we aim to address the topical societal challenge posed by algorithmic management. We generate knowledge on how algorithmic management affects work and employees, and develop methods based on research evidence for identifying and managing the related risks.

Timetable

7/2023–6/2029

Objectives

To enhance understanding of how algorithmic management affects safety, health, and well-being at work. 
To develop practical tools for identifying and managing risks related to algorithmic management.

Experts at the Finnish Institute of Occupational Health focus specifically on identifying cognitive and other psychosocial risks, and on understanding how these risks manifest in everyday work.

Data and methods

During autumn 2024 and spring 2025, the Finnish Institute of Occupational Health collected research data in two Finnish organizations. The data consists of interviews with employees, supervisors and other staff involved in the use of these systems, as well as observations of everyday work.

Results and impact

We address the challenges arising from the increasingly widespread use of algorithmic management in workplaces.

The research generates valuable knowledge on how algorithmic management affects safety, health and well-being at work. 
In addition to identifying risks and workload factors, we also examine the potential positive effects of algorithmic management on occupational safety, health and well-being.
The findings help support evidence-based decision-making in a constantly changing and evolving work environment.  
We are developing an easy-to-use checklist for occupational safety and health professionals and workplaces to help identify and assess risks and workload factors related to algorithmic management.