Abstract Thispaper explains how AI is changing the nature of labour from a task-based approach to automation. AI mainly changes some tasks inside jobs rather than completely eliminating occupations. New types of labour are emerging in fields like data governance, system monitoring, and human-machine coordination, even as regular and administrative tasks become more automated. These changes are not socially neutral, though. Large companies and capital owners typically benefit disproportionately from productivity gains brought about by AI, which exacerbates economic inequality and increases job instability for a sizable percentage of the workforce. Work is an essential social institution which has an impact on daily living, power dynamics, social identity, and financial survival. Artificial intelligence (AI) is changing how labour is organized, compensated, and experienced as it becomes more and more integrated into contemporary workplaces. Unlike previous technological advancements that mostly affected manual labour, artificial intelligence (AI) is creating a significant restructuring of both white-collar and blue-collar occupations by gradually influencing cognitive, analytical, and communicative skills. In order to comprehend how technological innovation interacts with social institutions and human experience, this article explores the relationship between AI and work from economic, sociological, and anthropological perspectives. This paper also emphasizes the risks of algorithmic bias in AI-based hiring, evaluation, and management systems. Because AI is trained on historical data that has been shaped by societal inequalities, it can reproduce and even exacerbate discrimination based on class, gender, caste, and race. These issues show that AI is not only a technology tool but also a socio-technical force shaped by institutional, political, and cultural choices. In order to ensure that AI promotes equitable, meaningful, and sustainable labour rather than increasing already-existing inequalities, the study highlighting the significance of inclusive education, lifelong learning, and ethical governance.
Building similarity graph...
Analyzing shared references across papers
Loading...
Goutam Singi (Sat,) studied this question.
www.synapsesocial.com/papers/6996a8a9ecb39a600b3efa76 — DOI: https://doi.org/10.5281/zenodo.18668543
Goutam Singi
Karnatak University
Building similarity graph...
Analyzing shared references across papers
Loading...