Chapter 28: Further Reading — AI and Employment

Foundational Research

1. Frey, C. B., & Osborne, M. A. (2017). "The future of employment: How susceptible are jobs to computerisation?" Technological Forecasting and Social Change, 114, 254–280. The landmark Oxford study estimating 47% of US jobs at high automation risk. Essential reading for understanding the methodology and its critiques. The paper's influence on public discourse makes understanding its methodology directly important.

2. Arntz, M., Gregory, T., & Zierahn, U. (2016). "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis." OECD Social, Employment and Migration Working Papers, No. 189. The OECD's competing analysis using task-level methodology that arrived at the substantially lower 14% estimate. Reading alongside Frey-Osborne illuminates the methodology debate clearly.

3. McKinsey Global Institute. (2017). "A Future That Works: Automation, Employment, and Productivity." McKinsey & Company. The MGI's influential business-oriented analysis of automation impact, focusing on work activities rather than occupations. Regularly updated; later versions incorporate AI-specific analysis.

4. Acemoglu, D., & Restrepo, P. (2022). "Tasks, Automation, and the Rise in US Wage Inequality." Econometrica, 90(5), 1973–2016. Rigorous economic analysis showing that automation has been a significant driver of wage inequality, with productivity gains accruing disproportionately to capital. Essential for the distributional effects analysis.

AI Augmentation and the Future of Work

5. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company. Accessible and influential treatment of AI and automation's economic effects, broadly optimistic about augmentation while acknowledging distributional challenges. A standard reference for business audiences.

6. Autor, D. H. (2015). "Why Are There Still So Many Jobs? The History, Nature, and Future of Workplace Automation." Journal of Economic Perspectives, 29(3), 3–30. Harvard economist David Autor's analysis of why automation has historically created as much work as it destroyed, and the conditions under which this might not continue. Clear and essential.

7. Noy, S., & Zhang, W. (2023). "Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence." Science, 381(6654), 187–192. Rigorous experimental study showing GPT-4 substantially raised the productivity of professional writers on business writing tasks, with the largest gains for lower-skilled writers — important evidence on augmentation dynamics.

Algorithmic Management and Worker Surveillance

8. Rosenblat, A. (2018). Uberland: How Algorithms Are Rewriting the Rules of Work. University of California Press. Comprehensive ethnographic study of Uber drivers and algorithmic management, based on years of fieldwork. Essential for understanding the gig economy from workers' perspectives.

9. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). "Algorithms at Work: The New Contested Terrain of Control." Academy of Management Annals, 14(1), 366–410. Systematic review of academic research on algorithmic management across workplace settings, providing conceptual frameworks for analyzing worker control dimensions.

10. Strategic Organizing Center. (2021). "The Injury Machine: How Amazon's Production System Hurts Workers." SOC. The detailed analysis of Amazon injury rates using Amazon's own OSHA 300 logs. Primary source material for the Amazon case.

Gig Economy and Worker Classification

11. Harris, S. D., & Krueger, A. B. (2015). "A Proposal for Modernizing Labor Laws for Twenty-First-Century Work: The 'Independent Worker'." Hamilton Project Discussion Paper. Influential policy proposal for a new intermediate employment category that would extend some but not all employment law protections to gig workers — represents the "third way" in classification debates.

12. Countouris, N. (2022). "The EU Platform Work Directive: An Assessment." European Trade Union Institute. Detailed analysis of the EU Platform Work Directive, its likely effects on worker classification, and its implementation challenges. Essential for international comparison.

Transition Policy

13. Dube, A. (2019). "Impacts of Minimum Wages: Review of the International Evidence." UK Government Independent Report. Comprehensive review of minimum wage research with implications for floor-setting in an AI-disrupted labor market.

14. Standing, G. (2017). Basic Income: And How We Can Make It Happen. Pelican Books. Accessible advocacy for UBI from a leading proponent, presenting the economic and social case. Read alongside critics for a balanced view.

15. Autor, D., Mindell, D., & Reynolds, E. (2022). "The Work of the Future: Building Better Jobs in an Age of Intelligent Machines." MIT Work of the Future. MIT's comprehensive assessment of AI and the future of work, with specific policy recommendations. More grounded in evidence than most treatments of the topic.

Creative Industries and AI

16. Writers Guild of America. (2023). "WGA Negotiating Committee Report on AI Provisions." WGA West. The union's own account of what they sought and achieved in AI provisions — primary source material. Available on the WGA website.

17. Karpf, A. (2023). "The Creativity Question." The Guardian, June 2023 series. Accessible exploration of what human creativity actually involves and what distinguishes it from AI generation — important for the creative labor displacement question.

Academic and Policy Deep Dives

18. Mishel, L., & Bivens, J. (2021). "The Zombie Robot Argument Lurches On." Economic Policy Institute. Critical assessment of automation-as-inequality-driver arguments, arguing that policy choices (union density, minimum wages, corporate governance) are more important than technology in determining distributional outcomes. Important counterweight to technological determinism.

19. Dosi, G., Pereira, M. C., Roventini, A., & Virgillito, M. E. (2021). "Technological Paradigms, Labour Creation and Destruction in a Multi-Sector Agent-Based Model." Research Policy, 50(4). Technical economic modeling of how technological transitions affect labor markets across sectors, for readers comfortable with economic methods.

20. Watkins, E. (2023). The Algorithm: How AI Can Reshape the Workforce. Harvard Business Review Press. Practitioner-oriented treatment for business executives on managing AI deployment with attention to workforce implications. Practical rather than academic.