COMPLEMENTARY COMPETITIVENESS: CRAFTING AN EMPLOYMENT POLICY TO ADDRESS TECHNOLOGICAL UNEMPLOYMENT IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

Technological unemployment has been a concern since the Industrial Revolution. Approximately two centuries later, this issue has reemerged with the rapid advancements in machine learning and artificial intelligence technologies (AI). In contrast to the Industrial Revolution era, the unemployment caused by AI in the present age is different. Unlike earlier times, where unemployment primarily resulted from automating basic manual labor, the current challenge arises from AI automating tasks that were previously considered too complex for machines to handle. This is due to the capacity of AI-powered machines to learn and adapt to new situations. As a result, the evolving job market necessitates a different approach to employment policies compared to those applied over the last century. In this study, a new policy suggestion referred to as "Complementary Competitiveness" is discussed, taking a nuanced stance, avoiding simplistic categorizations of AI as purely beneficial or detrimental. Instead, it concentrates on formulating an employment strategy that distinguishes between sectors, taking into account firms resaons’ of AI preferences, all while not impeding technological progress. This approach seeks to align employment policies with the evolving needs of the AI age, which goes beyond the conventional binary classification of professions and competencies as necessary or obsolete as it seen in the literature.