Artificial Intelligence and Inequality: Policy Paths in a Polarized Future
Hassan Daliri · Perspectives on Global Development and Technology · 2 Jul 2026
Where this sits in the debate
- Will AI widen inequality? · its finding supports the claim (why studies differ)
- Should governments act on AI now? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
An agent-based simulation shows that rapid automation without complementary skills investment sharply worsens income inequality and depresses labor's income share, while combining targeted skill subsidies with progressive taxation substantially reduces inequality at both ends of the distribution and shifts employment toward AI-complementary sectors. The core claim — that AI's distributional effects are policy-contingent rather than technologically predetermined — pushes back on deterministic 'AI will increase inequality' narratives.
Abstract
Abstract This study develops an agent-based simulation model to investigate how artificial intelligence (AI) and automation technologies affect income inequality under varying redistributive and human capital policies. Drawing on theories of skill-biased technological change, labor market polarization, and capital–labor substitution, the model captures dynamic interactions between heterogeneous agents, sectoral employment shifts, and evolving technological parameters. Simulation results reveal that rapid automation without complementary investment in skills (Scenario S2) exacerbates inequality, depresses labor income shares, and leads to pronounced employment displacement—mirroring trends observed in advanced economies. By contrast, policy configurations combining targeted skill subsidies with progressive taxation (Scenario S5) are shown to significantly reduce both top-end and bottom-end inequality, while facilitating a structural shift toward AI-complementary employment sectors. Sensitivity analysis underscores the importance of initial skill heterogeneity and learning efficiency in shaping long-term distributive outcomes. These findings provide compelling evidence that the inequality effects of AI are not technologically predetermined, but policy-contingent. The study highlights the critical role of redistribution and adaptive human capital strategies in aligning digital transformation with inclusive economic development.
About this publication
Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
- Published
- 2026 (4 months ago)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
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