Skip to main content
Log in

A definition, benchmark and database of AI for social good initiatives

  • Perspective
  • Published:

From Nature Machine Intelligence

View current issue Submit your manuscript

Abstract

Initiatives relying on artificial intelligence (AI) to deliver socially beneficial outcomes—AI for social good (AI4SG)—are on the rise. However, existing attempts to understand and foster AI4SG initiatives have so far been limited by the lack of normative analyses and a shortage of empirical evidence. In this Perspective, we address these limitations by providing a definition of AI4SG and by advocating the use of the United Nations’ Sustainable Development Goals (SDGs) as a benchmark for tracing the scope and spread of AI4SG. We introduce a database of AI4SG projects gathered using this benchmark, and discuss several key insights, including the extent to which different SDGs are being addressed. This analysis makes possible the identification of pressing problems that, if left unaddressed, risk hampering the effectiveness of AI4SG initiatives.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: Projects addressing the SDGs.
Fig. 2: Projects addressing different aspects of climate action.

Similar content being viewed by others

References

  1. Hager, G. D. et al. Artificial intelligence for social good. Preprint at https://arxiv.org/abs/1901.05406 (2017).

  2. Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. Deep learning for identifying metastatic breast cancer. Preprint at https://arxiv.org/abs/1606.05718 (2016).

  3. Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94–98 (2019).

    Article  Google Scholar 

  4. Rolnick, D. et al. Tackling climate change with machine learning. Preprint at https://arxiv.org/abs/1906.05433 (2019).

  5. Zhou, Y., Wang, F., Tang, J., Nussinov, R. & Cheng, F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit. Health 2, e667–e676 (2020).

    Article  Google Scholar 

  6. Hilbert, M. Big data for development: a review of promises and challenges. Dev. Policy Rev. 34, 135–174 (2016).

    Article  Google Scholar 

  7. Taylor, L. & Schroeder, R. Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 80, 503–518 (2015).

    Article  Google Scholar 

  8. Floridi, L. & Cowls, J. A unified framework of five principles for AI in society. Harv. Data Sci. Rev. 1, https://doi.org/10.1162/99608f92.8cd550d1 (2019).

  9. Taddeo, M. & Floridi, L. How AI can be a force for good. Science 361, 751–752 (2018).

    Article  MathSciNet  Google Scholar 

  10. Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11, 1–10 (2020).

    Article  Google Scholar 

  11. Chui, M. et al. Notes From The AI frontier: Insights From Hundreds Of Use Cases. (McKinsey Global Institute, 2018).

  12. International Telecommunication Union (ITU) AI Repository; https://www.itu.int/en/ITU-T/AI/Pages/ai-repository.aspx

  13. AI for Good Global Summit (28−31 May 2019, Geneva, Switzerland) (AI for Good, 2019); https://aiforgood.itu.int/

  14. Strickland, E. How IBM Watson overpromised and underdelivered on AI health care. In IEEE Spectrum: Technology, Engineering, and Science News https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care (IEEE, 2019).

  15. Ross, C. & Swetlitz, I. IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. In STAT https://www.statnews.com/2017/09/05/watson-ibm-cancer/ (5 September 2017).

  16. Goel, A. et al. Using Watson for enhancing human-computer co-creativity. In 2015 AAAI Fall Symp. Ser. (IEEE, 2015).

  17. Abebe, R. et al. Roles for computing in social change. In FAT* ’20: Proc. 2020 Conf. on Fairness, Accountability, and Transparency (ACM, 2019); https://doi.org/10.1145/3351095.3372871

  18. Green, B. ‘Good’ isn’t good enough. In Proc. AI for Social Good Worksh. NeurIPS (2019).

  19. United Nations Development Program (UNDP) Sustainable Development Goals. https://www.undp.org/content/undp/en/home/sustainable-development-goals.html (UNDP, 2015).

  20. Ram, A. Europe’s AI start-ups often do not use AI, study finds. Financial Times (5 March 2019).

  21. Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for deep learning in NLP. Proc. 57th Annual Meeting of the Association for Computational Linguistics 3645–3650 (ACL, 2019).

  22. Inter-American Development Bank fAIrLAC Observatory (IADB, 2020); https://fairlac.iadb.org/en/observatory

  23. Oxford Initiative on AI×SDGs. https://www.aiforsdgs.org/ (2020).

  24. Floridi, L., Cowls, J., King, T. C. & Taddeo, M. How to design AI for social good: seven essential factors. Sci. Eng. Ethics 26, 1771–1796 (2020).

    Article  Google Scholar 

  25. Dandres, T. et al. Consequences of future data center deployment in Canada on electricity generation and environmental impacts: A 2015–2030 prospective study. J. Ind. Ecol. 21, 1312–1322 (2017).

    Article  Google Scholar 

  26. Strubell, E., Ganesh, A. & McCallum, A. Energy and policy considerations for deep learning in NLP. Preprint at https://arxiv.org/abs/1906.02243 (2019).

  27. Shrestha, P. Leading energy and tech groups call for International Centre for AI, Energy and Climate. Energy Live News https://www.energylivenews.com/2019/08/20/leading-energy-and-tech-groups-call-for-international-centre-for-ai-energy-and-climate/ (20 August 2019).

Download references

Acknowledgements

J.C. acknowledges the receipt of a Doctoral Studentship from the Alan Turing Institute. M.T. and L.F. acknowledge the Oxford Initiative on AI for SDG, which is supported by grants from Facebook, Google and Microsoft.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luciano Floridi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cowls, J., Tsamados, A., Taddeo, M. et al. A definition, benchmark and database of AI for social good initiatives. Nat Mach Intell 3, 111–115 (2021). https://doi.org/10.1038/s42256-021-00296-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-021-00296-0

  • Springer Nature Limited

This article is cited by

Navigation