Executive Summary

Building a comprehensive database on AI-Driven automation, labor market shifts, and policy responses

Through systematic review and qualitative coding of hundreds of academic studies, the project builds a structured database mapping key trends in AI-driven automation, job displacement, skill shifts, and worker well-being. The research also examines policy responses including regulation, education, and worker protections. Undergraduate researchers play a central role in the coding and analysis process, gaining training in empirical research design, content analysis, and interdisciplinary policy scholarship. 

‍

A large-scale PRISMA systematic literature review

This project predicts and forecasts the impacts of AI and automation technology on workers through a large scale systematic literature review. The advance of AI continues to impact individual workers, firms, and societal trends, potentially changing the meaning of work, impacting productivity levels, and disrupting fundamental labor supply and demand dynamics. Our large-scale PRISMA systematic literature review parses all publications related to AI and automation, future forecasts, and workers published from 2010 to 2024. Our comprehensive empirical analysis on the impact of AI on workers implicates policymakers, business leaders, and workers alike.

‍

‍

Team Members:

Faculty: Daniel S. Schiff, Luísa Nazareno, Zeewan Lee

Graduate researchers: Lucas Wiese

Undergraduate researchers:

‍

For Prospective Team Members:

This project is a good fit for students interested in empirical policy research who want to engage deeply with interdisciplinary literatures across economics, sociology, policy, and technology studies. Students will develop expertise in research design, systematic reviews, and qualitative coding while reading and analyzing complex academic work from diverse fields.

‍