Algorithmic Management in Traditional

Workplaces: A Role for Labour Market Policy

Friday, November 17, 2023
Plenary Session
9:45 AM - 11:00 AM


In 2019, approximately 12% of European companies used artificial intelligence to manage or surveil their employees. As of 2022, up to one quarter of European companies could have used AI tools to support recruiting and hiring efforts. Algorithmic Management (AM) or the use of automated

solutions for the management of human labour input, is increasingly being incorporated by European business in every sector of the economy.

AM helps maximise efficiency, reduce costs and augment productivity. If well used, it may favour inclusivity and increase workers’ safety. However, if left unchecked, its misuse can undermine the founding principles of a just society and economy. Algorithms can lead to discrimination, lack of inclusivity, exploitation of the most vulnerable, with significant consequences on workers’ mental and physical health.

Public policy should aim to design the safeguards that are necessary to mitigate risks for workers, while at the same time enabling and incentivising the adoption of AM practices that can benefit them.

The opening plenary session of Day 2 will host world-renowned academics at the frontier of research on the impact of technology on labour markets. They will address questions such as:

· What is AM and what definitions are more suitable for the purpose of policy action?

· Which AM applications are most likely to be adopted in traditional workplaces?

· What are the benefits and risks that AM will bring to workers and employers?

· Is the current regulatory framework enough to tackle the emerging concerns?

· What should policy makers do to mitigate the risks while promoting the development and adoption of technologies that improve workers’ wellbeing and working conditions?

This session will set the ground for the ensuing workshops dedicated to  (1) surveillance and data protection in the workplace; (2) workers involvement in shaping algorithms and (3) breaking bias: navigating workplace discrimination with AI.