What is pro-worker AI?
A working definition from MIT Sloan
pro-worker AI (noun)
Generative artificial intelligence that complements humans and augments their skills.
Artificial intelligence will have a substantial impact on the future of work. So far, automation efforts seem likely to displace skilled workers and diminish worker voice, according to MIT professors Daron Acemoglu, David Autor, and Simon Johnson.
In a 2023 policy memo, the three professors — who lead the MIT Shaping the Future of Work Initiative — argued that the nature of AI’s impact on work and inequality is not inevitable; it depends on how society develops and shapes the technology. Used correctly, generative AI could create and support new occupational tasks and new capabilities for workers, especially for people without a four-year college degree.
“If AI tools can enable teachers, nurse practitioners, nurses, medical technicians, electricians, plumbers, and other modern craft workers to do more expert work, this can reduce inequality, raise productivity, and boost pay by leveling workers up,” they wrote.
The memo suggested policy changes, including updating Occupational Safety and Health Administration rules to limit worker surveillance, increasing funding for research on human-complementary technology, and equalizing the tax rates for employing workers and owning equipment and algorithms.
Working Definitions: Artificial Intelligence
MIT Sloan's Working Definitions explore the words and phrases behind emerging management ideas.
Leading the AI-Driven Organization
In person at MIT Sloan
Register Now
These are the most urgent AI risks, according to 272 experts
Which AI risks could cause the most harm in the next five years? New MIT research shows that businesses should be aware of threats like competitive pressure and dangerous AI capabilities.
5 ways to make agentic AI a competitive advantage
Enterprises looking to make the most of agentic AI will have to rethink how work gets done and how teams are organized, without forgetting the human workers who set their companies apart.
Who will own the AI agent economy?
Here’s what businesses need to know as AI agents move from centralized systems toward a decentralized network of trillions of personal and organizational agents.
5 investments to close the gap between AI wealth and welfare
Transformative technologies like artificial intelligence succeed when societies make parallel social investments to ensure gains are distributed equitably, MIT Sloan researchers find.
Meet the new faculty members joining MIT Sloan in 2026
Debiased machine learning, the currency of invoicing, and training good models with bad data: Meet the new experts bringing their knowledge and skill sets to the MIT Sloan School of Management.
Data liquidity leads to AI success
Three levers — data architecture, data preparation, and data permissions — determine whether data becomes a reusable strategic asset or stays trapped in silos.
Pro-worker AI, explained
Artificial intelligence can make workers more capable and productive, but only if leaders design and deploy it to augment human judgment.
5 things to consider when working with AI
Researchers at the MIT Initiative on the Digital Economy share the latest insights about getting the most from working with AI, such as personality pairing and reorganizing job tasks.
Balance AI innovation and risk with ‘minimum viable governance’
As organizations scale generative AI, traditional governance models prove to be too rigid or too loose. Minimum viable governance calibrates oversight to risk, enabling responsible innovation.
The surprising power of warmth in AI negotiations
In MIT’s international AI Negotiation Competition, “warmer” agents achieved better outcomes in negotiations with other AI agents.