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March 3, 2022

Economic impacts research at OpenAI

Call for expressions of interest to study the economic impacts of large language models.

Abstract watercolor painting of a sunset over a city, generated by DALL·E 2

Illustration: Justin Jay Wang × DALL·E

Core to our mission of ensuring that artificial general intelligence benefits all of humanity is understanding the economic impacts that our models will have or are having on individuals and society as a whole. Developing tools to rigorously measure the economic impacts of our models is essential to making smarter development and deployment decisions and critical to informing public policy options that maximize human prosperity and minimize the risk of economic harms from AI. Our ability to generate high quality evidence to inform these decisions will be greatly enhanced by developing a range of productive research partnerships, and we firmly believe that AI developers need to support external researchers undertaking this work, rather than exclusively conducting research in-house.

Under this premise, you can see our first public research agenda on these topics(opens in a new window). This describes our preliminary priorities for research on the economic impacts of large language models broadly. We are excited to complement this research agenda with concrete action to facilitate improved measurement of the economic impacts of our models. We are launching a call for expressions of interest from researchers interested in studying the economic impacts of Codex and our other large language model releases like GPT-3, ChatGPT, and DALL-E 2 and a portal for customers to submit interest in supporting this work. You can find more information on both below.

Importance of studying economic impacts

As an AI research and deployment company, OpenAI recognizes that our decisions around AI system design and deployment can influence economic impacts and the distribution of economic benefits from advances in AI. Despite remarkable technological progress over the past several decades, gains in economic prosperity have not been widely distributed. In the US, trends in both income and wealth inequality over the last forty years demonstrate a worrying pace of economic divergence and uneven access to opportunity.1 While recent evidence suggests that there is little immediate risk of widespread technological unemployment due to AI, it is clear that the labor market impacts of increasingly advanced AI will vary widely across different types of workers.2 Unemployment shocks, even if transitory, have been shown to have widespread negative effects on individual wellbeing,3 and increasing economic inequality may amplify societal cleavages.4

We are eager to support and conduct research that has the potential to impact decision-making on three axes:

  1. AI deployment policies

  2. AI system design decisions

  3. Public policy interventions that draw upon real-world evidence.

We need to engage in research about the economic impact of our models today in order to be positioned to assess the safety of developing and releasing more capable systems in the future. Our recent model releases such as Codex, DALL·E 2, and ChatGPT provide tractable opportunities to establish the foundation for this research going forward.

Submission process for researchers

We are currently seeking submissions from PhD-level researchers, including current doctoral students. Research collaborations could take many forms, from being connected to our larger research cohort, to being invited to participate in red teaming exercises, to receiving API credit grants. We will work with potential collaborators to tailor our partnership to ensure the most productive working relationship. Down the line, we would like to be able to connect researchers to customers who are building products on top of large language models in direct contact with and that directly impact workers and consumers.

When evaluating expressions of interest, we will assess your background and experience, clarity of motivation to collaborate with OpenAI, and both the clarity and decision-relevance of your research interests to the economic impact of large language models.

If you would like to submit an expression of interest to be a Research Collaborator please use this form(opens in a new window).

Submission process for companies and users of OpenAI tools

We are also in the process of connecting researchers with firms that are best equipped to support particular research interests. We intend to facilitate discussions among external researchers, AI developers, AI-adopting firms, and workers in various industries that have been affected by advances in AI in an effort to widen the range of perspectives that can shape the path of AI development and deployment.

If you are a company or user of our models or products and want to learn how you can contribute to or sponsor research on economic impacts of AI systems, please fill out this form(opens in a new window).

Additional information

If you have any questions about the submission forms or the call for expressions of interest, please contact us at econ@openai.com.

References

  1. 1

    Chetty, Raj, et al. “The fading American dream: Trends in absolute income mobility since 1940.” Science 356.6336 (2017): 398-406.; Saez, Emmanuel, and Gabriel Zucman. “The rise of income and wealth inequality in America: Evidence from distributional macroeconomic accounts.” Journal of Economic Perspectives 34.4 (2020): 3-26.

  2. 2

    Autor, David, David Mindell, and Elisabeth Reynolds. “The work of the future: Building better jobs in an age of intelligent machines.” Boston: MIT. https://workofthefuture.mit.edu/wp-content/uploads/2021/01/2020-Final-Report4.pdf(opens in a new window) vom 18 (2020): 2020.

  3. 3

    Brand, Jennie E. “The far-reaching impact of job loss and unemployment.” Annual review of sociology 41 (2015): 359-375.

  4. 4

    Van de Werfhorst, Herman G., and Wiemer Salverda. “Consequences of economic inequality: Introduction to a special issue.” Research in Social Stratification and Mobility 30.4 (2012): 377-387.

Authors

Sam Manning, Pamela Mishkin, Tyna Eloundou