Search

Begin New Search Topic Inclusion Author Laura Nurski

1. Artificial intelligence adoption in the public sector: a case study

Author:
Laura Nurski
Publication Date:
03-2023
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
This case study illustrates the drivers of and barriers to artificial intelligence adoption by organisations, and acceptance of AI by workers in the public sector. Several factors were crucial in the successful adoption of a human-centred approach to AI, including a fast discovery phase that involved workers (or end users) in the development early on, and aligning human resources, information technology and business processes. Subsidy support mechanisms were also specifically targeted and acquired to support the adoption. However, making AI support available to workers proved insufficient to ensure its widespread usage throughout the organisation. The slow adaptation of existing work processes and legacy IT systems was a barrier to the optimal usage of the technology. Moreover, the usefulness of the technology depended on both the task routineness and worker experience, thereby necessitating a rethinking of the work division between technology and workers, and between junior and senior workers. Successful human-centred roll-out of AI in Europe will therefore depend on the availability of, or investments in, complementary intangible organisational capital. Very little is currently known about these investments.
Topic:
Digital Economy, Economic Growth, Public Sector, Innovation, Artificial Intelligence, Inclusion, and Work
Political Geography:
Global Focus

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

Author:
Laura Nurski and Mia Hoffmann
Publication Date:
07-2022
Content Type:
Working Paper
Institution:
Bruegel
Abstract:
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.
Topic:
Science and Technology, Economy, Artificial Intelligence, Inclusion, and Work
Political Geography:
Global Focus

3. Beating burnout: identifying bad jobs and improving job quality

Author:
Mia Hoffmann and Laura Nurski
Publication Date:
05-2022
Content Type:
Policy Brief
Institution:
Bruegel
Abstract:
While European-wide information on burnout is scarce, national statistics show that stress-related absenteeism is on the rise, generating significant costs for firms and welfare states, while reducing worker wellbeing. Although manifested at the individual level, burnout is an occupational phenomenon, predicted most clearly by imbalances in job content (high workloads and low autonomy) and the social environment at work – two under-explored aspects of job quality. While the economy and society as a whole would benefit from a healthier workforce, market failures drive job quality below an optimal level, necessitating attention from policymakers. Measuring and intervening in job content is not straightforward, however, and has not been a main policy domain in Europe. Policy frameworks and interventions therefore tend to focus on other areas of job quality, such as the physical and contractual working conditions. To manage the burnout epidemic and mitigate the impact of the changing nature of work, job-quality policy needs to focus on the job-content aspects as well. Wellbeing outcomes of low job quality, such as burnout, need to be monitored at European level and can serve to evaluate the effectiveness of policy interventions in job quality.
Topic:
Labor Issues, Employment, Economic Growth, Inclusion, Workforce, and Well-Being
Political Geography:
Europe