Guidelines for AI and Shared Prosperity



Home

Our economic future is too important to leave to chance.

AI has the potential to radically disrupt people’s economic lives in both positive and negative ways. It remains to be determined which of these we’ll see more of. In the best scenario, AI could widely enrich humanity, equitably equipping people with the time, resources, and tools to pursue the goals that matter most to them.

Our current moment serves as a profound opportunity — one that we will miss if we don’t act now. To achieve a better future with AI, we must put in the work today.

In medicine and other fields, new innovations are put through rigorous testing to ensure they are fit for purpose. The AI community, however, has no established practice for assessing the impact of AI systems on inequality or job quality. Without one, it remains difficult to ensure AI deployments are bringing us closer to the economic future we want to live in.

You can help guide AI’s impact on jobs

AI developers, AI users, policymakers, labor organizations, and workers can all help steer AI so its economic benefits are shared by all. Using Partnership on AI’s (PAI) Shared Prosperity Guidelines, these stakeholders can minimize the chance that individual AI systems worsen shared prosperity-relevant outcomes.

The Shared Prosperity Guidelines can be used by following a guided, three-step process.

 

 


AI-Creating Organizations ➜


AI-Using Organizations ➜


Policymakers ➜


Labor Organizations and Workers ➜

Download the Guidelines

Get Involved

Partnership on AI needs your help to refine, test, and drive adoption of the Guidelines for AI and Shared Prosperity.

Fill out the form below to share your feedback on the Guidelines, ask about collaboration opportunities, and receive updates about events and other future work by the AI and Shared Prosperity Initiative.

Get in Touch

Guidelines for AI and Shared Prosperity

Home

Step 1: Learn About the Guidelines

The Need for the Guidelines

The Origin of the Guidelines

Design of the Guidelines

Key Principles for Using the Guidelines

Step 2: Apply the Job Impact Assessment Tool

Instructions for Performing a Job Impact Assessment

Signals of Opportunity to Advance Shared Prosperity

Signals of Risk to Shared Prosperity

STEP 3: Stakeholder-Specific Recommendations

For AI-Creating Organizations

For AI-Using Organizations

For Policymakers

For Labor Organizations and Workers

Get Involved

Endorsements

Acknowledgments

AI and Shared Prosperity Initiative’s Steering Committee

Sources Cited

  1. ​​Acemoglu, D. (Ed.). (2021). Redesigning AI: Work, democracy, and justice in the age of automation. Boston Review.
  2. Korinek, A., and Stiglitz, J.E. (2020, April). Steering technological progress. In NBER Conference on the Economics of AI.
  3. Acemoglu, D., and Johnson, S. (2023). Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. Public Affairs, New York.
  4. International Labour Organization. (n.d.). Decent work. https://tinyurl.com/yur776yd
  5. US Department of Commerce and US Department of Labor. (n.d.). Department of Commerce and Department of Labor Good Jobs Principles, DOL. https://tinyurl.com/mtbpemkn
  6. Institute for the Future of Work. (n.d.). The Good Work Charter. https://tinyurl.com/ycxtaax4
  7. Klinova, K., and Korinek, A. (2021). AI and shared prosperity. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 645-651).
  8. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  9. Partnership on AI, 2021. Redesigning AI for Shared Prosperity: an Agenda. https://partnershiponai.org/paper/redesigning-ai-agenda/
  10. Negrón, W. (2021). Little Tech is Coming for Workers. Coworker.org. https://home.coworker.org/wp-content/uploads/2021/11/Little-Tech-Is-Coming-for-Workers.pdf.
  11. Korinek, A., 2022. How innovation affects labor markets: An impact assessment.
  12. Brynjolfsson, E., Collis, A., Diewert, W.E., Eggers, F., and Fox, K.J. (2019). GDP-B: Accounting for the value of new and free goods in the digital economy (No. w25695). National Bureau of Economic Research.
  13. Acemoglu, D., and Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30.
  14. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  15. Valentine, M., and Hinds, R. (2022). How Algorithms Change Occupational Expertise by Prompting Explicit Articulation and Testing of Experts’ Theories. https://tinyurl.com/pxyr8ev3
  16. Autor, D. (2022). The labor market impacts of technological change: From unbridled enthusiasm to qualified optimism to vast uncertainty (No. w30074). National Bureau of Economic Research.
  17. Mateescu, A., and Elish, M. (2019). AI in context: the labor of integrating new technologies.
  18. Elish, M. C. (2019). Moral crumple zones: Cautionary tales in human-robot interaction (pre-print). Engaging Science, Technology, and Society (pre-print).
  19. World Bank. (2017). World development report 2018: Learning to realize education's promise. The World Bank.
  20. Korinek, A., and Stiglitz, J.E. (2021). Artificial intelligence, globalization, and strategies for economic development (No. w28453). National Bureau of Economic Research.
  21. Diao, X., Ellis, M., McMillan, M. S., and Rodrik, D. (2021). Africa's manufacturing puzzle: Evidence from Tanzanian and Ethiopian firms (No. w28344). National Bureau of Economic Research.
  22. Rodrik, D. (2022). 4 Prospects for global economic convergence under new technologies. An inclusive future? Technology, new dynamics, and policy challenges, 65.
  23. O'Keefe, C., Cihon, P., Garfinkel, B., Flynn, C., Leung, J., and Dafoe, A. (2020, February). The windfall clause: Distributing the benefits of AI for the common good. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 327-331).
  24. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  25. Scherer, M., and Brown, L. X. (2021). Warning: Bossware May Be Hazardous to Your Health. Center for Democracy and Technology. https://cdt.org/wp-content/uploads/2021/07/2021-07-29-Warning-Bossware-May-Be-Hazardous-To-Your-Health-Final.pdf
  26. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  27. Acemoglu, D., and Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973-2016.
  28. Valentine, M., and Hinds, R. (2022). How Algorithms Change Occupational Expertise by Prompting Explicit Articulation and Testing of Experts’ Theories. https://tinyurl.com/pxyr8ev3
  29. Nurski, L., and Hoffmann, M. (2022). The Impact of Artificial Intelligence on the Nature and Quality of Jobs. Working Paper. Bruegel. https://tinyurl.com/jxayzdcz
  30. Pritchett, L. (2020). The future of jobs is facing one, maybe two, of the biggest price distortions ever. Middle East Development Journal, 12(1), 131-156.
  31. Eloundou, T., Manning, S., Mishkin, P., and Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
  32. Noy, S., and Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4375283
  33. Korinek, A. (2023). Language models and cognitive automation for economic research (No. w30957). National Bureau of Economic Research.
  34. Case, A., and Deaton, A. (2020). Deaths of Despair and the Future of Capitalism. Princeton University Press.
  35. Gihleb, R., Giuntella, O., Stella, L., and Wang, T. (2022). Industrial robots, workers’ safety, and health. Labour Economics, 78, 102205.
  36. Pritchett, L. (2020). The future of jobs is facing one, maybe two, of the biggest price distortions ever. Middle East Development Journal, 12(1), 131-156.
  37. Pritchett, L. (2023). Choose People. LaMP Forum. https://lampforum.org/2023/03/02/choose-people/
  38. Gray, M. L., and Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Eamon Dolan Books.
  39. Dubal, V. (2023). On Algorithmic Wage Discrimination. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4331080
  40. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  41. Schneider, D., and Harknett, K. (2017, April). Schedule Instability and Unpredictability and Worker and Family Health and Well-being. In PAA 2017 Annual Meeting. PAA.
  42. Williams, J. et al. (2022). Stable scheduling study: Health outcomes report. https://ssrn.com/abstract=4019693
  43. Bell, S. A. (2022). AI and Job Quality: Insights from Frontline Workers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337611
  44. Dzieza, J. (2020). Robots aren’t taking our jobs — They’re becoming our bosses. The Verge. https://tinyurl.com/5a9mxeuz
  45. Levy, K. (2022). Data Driven: truckers, technology, and the new workplace surveillance. Princeton University Press.
  46. Moore, P.V. (2017). The quantified self in precarity: Work, technology and what counts. Routledge.
  47. Scherer, M., and Brown, L. X. (2021). Warning: Bossware May Be Hazardous to Your Health. Center for Democracy and Technology. https://cdt.org/wp-content/uploads/2021/07/2021-07-29-Warning-Bossware-May-Be-Hazardous-To-Your-Health-Final.pdf.
  48. Brand, J., Dencik, L. and Murphy, S. (2023). The Datafied Workplace and Trade Unions in the UK. Data Justice Lab. https://datajusticeproject.net/wp-content/uploads/sites/30/2023/04/Unions-Report_final.pdf.
  49. Nurski, L., and Hoffmann, M. (2022). The Impact of Artificial Intelligence on the Nature and Quality of Jobs. Working Paper. Bruegel. https://tinyurl.com/2a943p8f
  50. Nanavaty, R. (2023). Interview with Reema Nanavaty, Self-Employed Women’s Association.
  51. Beane, M. (2022). Today's Robotic Surgery Turns Surgical Trainees into Spectators: Medical Training in the Robotics Age Leaves Tomorrow's Surgeons Short on Skills. IEEE Spectrum, 59(8), 32-37. https://tinyurl.com/wyhxukhk
  52. Gray, M. L., and Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Eamon Dolan Books.
  53. Center for Democracy and Technology et al. 2022
  54. Buolamwini, J., and Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.
  55. Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. John Wiley and Sons.
  56. Keyes, O. (2018). The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on human-computer interaction, 2(CSCW), 1-22.
  57. Rosales, A., and Fernández-Ardèvol, M. (2019). Structural ageism in big data approaches. Nordicom Review, 40(s1), 51-64.
  58. Klinova, K. (2022) Governing AI to Advance Shared Prosperity. In Justin B. Bullock et al. (Eds.), The Oxford Handbook of AI Governance. Oxford Handbooks.
  59. Park, H., Ahn, D., Hosanagar, K., and Lee, J. (2021, May). Human-AI interaction in human resource management: Understanding why employees resist algorithmic evaluation at workplaces and how to mitigate burdens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
  60. Bernhardt, A., Suleiman, R., and Kresge, L. (2021). Data and algorithms at work: the case for worker technology rights. https://laborcenter.berkeley.edu/wp-content/uploads/2021/11/Data-and-Algorithms-at-Work.pdf.
  61. Colclough, C.J. (2022). Righting the Wrong: Putting Workers’ Data Rights Firmly on the Table. https://tinyurl.com/26ycnpv2
  62. Pasquale, F. (2020). New Laws of Robotics. Harvard University Press.
  63. Rodrik, D. (2022). 4 Prospects for global economic convergence under new technologies. An inclusive future? Technology, new dynamics, and policy challenges, 65.
  64. Anderson, E. (2019). Private Government: How Employers Rule Our Lives (and Why We Don’t Talk about it). Princeton University Press.
  65. Korinek, A. (2022). How innovation affects labor markets: An impact assessment.
  66. Institute for the Future of Work. (2023). Good Work Algorithmic Impact Assessment Version 1: An approach for worker involvement. https://tinyurl.com/mr4yn5yt
  67. Bernhardt, A., Suleiman, R., and Kresge, L. (2021). Data and algorithms at work: the case for worker technology rights. https://laborcenter.berkeley.edu/wp-content/uploads/2021/11/Data-and-Algorithms-at-Work.pdf.
  68. Colclough, C.J. (2022). Righting the Wrong: Putting Workers’ Data Rights Firmly on the Table. https://tinyurl.com/26ycnpv2
  69. Brand, J., Dencik, L. and Murphy, S. (2023). The Datafied Workplace and Trade Unions in the UK. Data Justice Lab. https://datajusticeproject.net/wp-content/uploads/sites/30/2023/04/Unions-Report_final.pdf.
  70. Park, H., Ahn, D., Hosanagar, K., and Lee, J. (2021, May). Human-AI interaction in human resource management: Understanding why employees resist algorithmic evaluation at workplaces and how to mitigate burdens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
  71. Mateescu, A., and Elish, M. (2019). AI in context: the labor of integrating new technologies.
  72. Elish, M. C. (2019). Moral crumple zones: Cautionary tales in human-robot interaction (pre-print). Engaging Science, Technology, and Society (pre-print).
Table of Contents