AI for a More Human Friendly Workplace Recovery After COVID-19

Beryl Haar () and Marta Otto ()
Additional contact information
Beryl Haar: University of Warsaw
Marta Otto: University of Warsaw

Chapter Chapter 7 in Work Beyond the Pandemic, 2024, pp 113-132 from Springer

Abstract: Abstract For many workers the world of work changed almost overnight when in early 2020 we had to switch to remote working due to COVID-19 lock down measures of governments. While remote working felt revolutionary for many, when considered in the world of automation, it is just the tip of the “iceberg”. An increasing number of scholars in economics, politics, and labour law argue that we are at the brink of a fundamental change in the evolving relationship between people and technology. So far this has resulted in responses in legal doctrine and by legislators of a “risk-based approach”. In this contribution, we will explore a yet rarely frequented path: one which focuses on the positive aspects automation has to offer and how it can help us create a more human-friendly workplace, including new forms of regulation based on safeguarding and fostering (new) twenty-first-century fundamental labour values.

Keywords: Artificial intelligence; Human-centred AI; Society 5.0; Industry 5.0; AI-human workforce; Risk-based regulation (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-39951-0_7

Ordering information: This item can be ordered from
http://www.springer.com/9783031399510

DOI: 10.1007/978-3-031-39951-0_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().