Artificial intelligence (AI) is often portrayed as a powerful force that can automate jobs, leading to widespread displacement of workers. However, there’s another side to the AI coin: its potential to empower employees and enhance their capabilities. By collaborating with workers, AI can amplify effectiveness in existing roles, facilitate new tasks, and even help individuals acquire new skills. This means AI can be more than just a job disruptor; it can serve as a transformative tool that enhances human expertise.
A Conceptual Framework for Pro-Worker AI
To understand the types of innovations that likely benefit workers, we need to categorize them into five economically distinct groups:
- Labor-augmenting technologies enhance worker effectiveness in their current roles.
- Capital-augmenting technologies improve the functionality and efficiency of machines, making them better, cheaper, or faster in their existing roles.
- Automating technologies change the traditional division of labor by replacing tasks previously performed by humans with machines or algorithms.
- Expertise-leveling technologies empower a broader range of workers to tackle tasks that previously required specialized knowledge.
- New task-creating technologies generate entirely new human roles and responsibilities.
Among these categories, new task-creating technologies are unequivocally beneficial for workers, generating demand for innovative human expertise. Unlike other types, they expand both the variety and the volume of work while enhancing the value of the skills needed for these new tasks. Conversely, automating technologies tend to have negative implications for labor markets, potentially leading to job losses and increased inequality. This doesn’t necessarily make them socially undesirable, but it does invite consideration of the trade-offs involved in the labor market landscape.
Why Isn’t Pro-Worker AI Everywhere?
While pro-worker tools primarily benefit both workers and firms by enabling greater productivity, there’s a puzzling scarcity of investment in pro-worker AI developments. Much of the current focus within the AI industry leans toward automating tasks and creating high-level capabilities aligned with artificial general intelligence (AGI). This raises the question: Why is there so much less investment in pro-worker AI?
One significant factor is economic incentives. Companies often perceive greater financial returns from automating expertise than from creating new tasks or boosting the value of existing skills. The allure of achieving AGI, where machines can comprehensively outperform human capabilities, further diverts attention and resources away from the development of pro-worker innovations. While it’s not solely the responsibility of individual companies to ensure AI benefits all workers, it is a shared societal interest that requires collective action for an inclusive AI-focused future.
Moving the Needle Toward Pro-Worker AI
There are nine targeted public policy strategies that could effectively steer advances in AI toward a pro-worker orientation:
- Shaping pro-worker AI in health care and education. These sectors present abundant opportunities for AI to enhance human roles significantly, warranting focused investment.
- Building AI expertise in government. Federal and state governments must set clear goals for AI deployment and cultivate expertise within their ranks to achieve these objectives.
- Using grant-making to support worthwhile AI investments. Targeted grants can guide AI development toward pro-worker outcomes, encouraging innovations that benefit employees.
- Fostering competition for excellence. Emulating a Defense Advanced Research Projects Agency (DARPA) competitive prize model in R&D could spur innovation aimed at pro-worker solutions.
- Rebalancing the tilted investment playing field in the tax code. Policies should align tax structures for hiring and training workers with those for equipment and software investments.
- Fostering private sector competition. Enhanced antitrust enforcement can stimulate competition in technology development, allowing for more pro-worker business models.
- Harnessing worker voice. Institutional mechanisms for worker input are essential to ensure that their needs and perspectives shape AI innovations.
- Discouraging “expertise theft.” Establishing legal frameworks that respect workers’ ownership of their skills can foster an environment where human expertise is valued.
- Loosening up licensure. Rethinking licensure requirements can lower barriers to entry for new talent and facilitate a more inclusive and dynamic workforce.
