Working paper

The impact of artificial intelligence on the nature and quality of jobs

Policymakers should strengthen the role of social partners in the adoption of AI technology to protect workers’ bargaining power.

Publishing date
26 July 2022
j

Artificial intelligence (AI), like any workplace technology, changes the division of labour in an organisation and the resulting design of jobs. When used as an automation technology, AI changes the bundle of tasks that make up an occupation. In this case, implications for job quality depend on the (re)composition of those tasks. When AI automates management tasks, known as algorithmic management, the consequences extend into workers’ control over their work, with impacts on their autonomy, skill use and workload. We identify four use cases of algorithmic management that impact the design and quality of jobs: algorithmic work-method instructions; algorithmic scheduling of shifts and tasks; algorithmic surveillance, evaluation and discipline; and algorithmic coordination across tasks. 

Reviewing the existing empirical evidence on automation and algorithmic management shows significant impact on job quality across a wide range of jobs and employment settings. While each AI use case has its own particular effects on job demands and resources, the effects tend to be more negative for the more prescriptive (as opposed to supportive) use cases. These changes in job design demonstrably affect the social and physical environment of work and put pressure on contractual employment conditions as well.

As technology development is a product of power in organisations, it replicates existing power dynamics in society. Consequently, disadvantaged groups suffer more of the negative consequences of AI, risking further job-quality polarisation across socioeconomic groups. Meaningful worker participation in the adoption of workplace AI is critical to mitigate the potentially negative effects of AI adoption on workers, and can help achieve fair and transparent AI systems with human oversight. Policymakers should strengthen the role of social partners in the adoption of AI technology to protect workers’ bargaining power.

About the authors

  • Laura Nurski

    Laura Nurski leads the Future of Work and Inclusive Growth project which analyses the impact of technology on the nature, quantity and quality of work, welfare systems and inclusive growth.

    Before joining Bruegel, she investigated the impact of job design and organisation design on wellbeing and productivity at work. This inherently multidisciplinary domain has left her with a broad social science background, encompassing psychology, sociology and economics.

    Laura is passionate about data and technology. As a former data scientist in the financial and retail sector, she developed machine learning models and big data analytics. She is also a skilled statistical programmer, survey developer and open-source aficionado.

    Laura holds a Ph.D. in Industrial Organization, a M.Sc. in Economics and a M.A. in Business Engineering from KU Leuven.

  • Mia Hoffmann

    Mia worked at Bruegel as a Research Analyst. She studied International Economics (BSc) at University of Tuebingen, including one semester at the Università di Torino, and holds a Master’s degree in Economics from Lund University.

    Before joining Bruegel Mia worked in the international development sector. As a Bluebook Trainee she worked at the European Commission’s Directorate-General for International Cooperation and Development and previously interned at the German development bank DEG, working on credit analysis and restructuring.

    Her previous research focused on the impact of migration on economic growth and analyzed the effects of childcare policy on household bargaining. Her current research interests involve issues related to trade, labor markets and inequality.

    Mia is a German native speaker, is fluent in English and has good working knowledge in French and Italian.

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