How AI can Impact Demand and Supply of Labour(Musings)

Mapping the Interplay Between Artificial Intelligence, Productivity Growth, and Labor Force Participation

In this essay, I will be exploring the potential impacts of artificial intelligence (AI) on the demand and supply forces in labour markets. I aim to analyze how the proliferation of AI systems across various occupations could influence equilibrium dynamics between labour demand from employers and labour supply from workers.

However, I fear this analysis may lack sufficient rigour, as it is derived mainly from cursory notes I have compiled while researching this expansive topic. The interplay between AI adoption and macro employment trends is complex, with many open questions remaining. Still, I hope these provisional thoughts can serve as a starting point for further inquiry even if incomplete or speculative at this stage. I welcome feedback on strengthening the conceptual foundations or real-world evidence bases for the projections I discuss.

On the labour supply side, it is apparent that certain occupations have a greater susceptibility to AI-driven automation based on their skill demands and activities. Unfortunately, demographic data indicates that minority groups like Black and Hispanic workers are overrepresented in secretary, food service, transportation, and manufacturing roles with high technical viability for replacement by intelligent algorithms and robots. This exhibits how communities already facing structural barriers can experience disproportionate job vulnerability during AI-induced market transitions.

However, rather than resulting from biased systems, occupational imbalance leading to uneven automation risk across groups emerges from pre-existing socioeconomic disparities that funnel minorities toward lower-wage vocations. Reskilling, educational, and social support programs are imperative so vulnerable populations are not left behind during periods of AI transformation.

On the labour demand side, intriguing studies indicate AI integration can negatively skew perceived expertise and trust in human practitioners across areas from medicine to education. For example, diagnoses relying on AI assistance are generally viewed as less skilful even when accuracy improves. This “auditory evaluation” effect demonstrates how AI augmentation risks diminishing the prestige and value ascribed to human experts across industries.

Moreover, research shows diminishment is particularly acute for already marginalized groups. Patient assessments showed greater distrust in the competence of Black physicians when utilizing AI tools relative to white physicians with the same support system. Such disparities illustrate how algorithmic biases could compound negative judgment. Responsible development mandates assessing how AI perception varies across user demographics before deployment.

End Note

This analysis only scratches the surface of AI’s multifaceted societal impacts. Many open questions remain regarding mitigating risks and channeling benefits. But we must begin proactively assessing challenges, however complex. If guided prudently, AI can elevate equity and potential. But progress demands nuance, ethics and collective responsibility across technology, business, government and communities.