AI Trust and Technology Optimism in the Workforce: Data-Driven Insights into Regional Variation

Automation and AI systems are reshaping the workplace. How these technologies make a difference varies according to local contexts. Workers’ willingness to trust and embrace these technologies is shaping how this transformation unfolds in practice. Some workers trust AI more than others, and interestingly, trust levels differ from one region to another. Drawing on a far-reaching 2024 worker survey spanning different countries, and on a rich body of literature on technology, trust, and change, this work examines how key factors influencing workers’ AI trust and technology optimism interweave, shaping their perspectives on new technologies and automation. The focus is on understanding how the industrial and regulatory landscape in which workers operate, combined with their personal experiences with AI, shapes their AI optimism, with a particular emphasis on the US and Europe. While external market innovation indicators provide limited understanding of workers’ technology optimism, individual interaction and familiarity with AI, alongside organizational AI adoption and a worker’s industry of employment, emerge as key factors shaping AI trust. Additionally, the regulatory environment, encompassing technology governance, social safety nets, and workers’ institutional trust, all seem connected with how workers think about the impact of new technologies on society, the economy, and their jobs. Interpersonal trust propensity contributes to AI trust formation, though its relevance exhibits regional variation. By offering insights into the critical factors shaping the relationship between workers and AI, this study aims to provide evidence that supports societies in unlocking the value of emerging technologies, while empowering the workforce to confidently embrace and excel alongside them.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Velonia Bellonia, Maria Eleni

Advisor dc:contributor.advisor

• Armstrong, Ben

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162404

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162404