CoChat
Sign Up
Cited by 357Ethics and Social Impacts of AIArtificial Intelligence ApplicationsInnovation, Technology, and Society

Ai for social good: unlocking the opportunity for positive impact

Open access
Tomašev, Nenad, Cornebise, Julien, Hutter, Frank, Mohamed, Shakir, Picciariello, Angela, Connelly, Bec, Belgrave, Danielle C. M., Ezer, Daphne, Haert, Fanny Cachat van der, Mugisha, Frank, Abila, Gerald, Arai, Hiromi, Almiraat, Hisham, Proskurnia, Julia, Snyder, Kyle, Otake-Matsuura, Mihoko, Othman, Mustafa, Glasmachers, Tobias, Wever, Wilfried de, Teh, Yee Whye, Khan, Mohammad Emtiyaz, Winne, Ruben De, Schaul, Tom, Clopath, Claudia·2020·Nature Communications
DOI 10.1038/s41467-020-15871-zPMC PMC7235077
Open this paper in CoChat — free accountOpen PDF

Abstract

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-15871-z

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Active learning allows for the integration of users' feedback while models are learned, and enables interactivity with the user.

    “Active learning allows for the integration of users' feedback while models are learned, and enables interactivity with the user, one of the goals targeted by human-centered AI [77] and AI for social good [23].”

    From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [23]

    The Shaqodoon case study (page unavailable) emphasizes involving communities and describes an interactive platform for citizen feedback. The Call for action (page unavailable) says application-domain experts should inform AI-system design. These passages support participation as an AI-for-social-good goal, but they do not say that active learning incorporates user feedback while models are being learned. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Various scenarios have been studied to apply AI to advance the United Nations' Sustainable Development Goals (SDGs) for social good.

    “For example, as noted in [78, 62], various scenarios have been studied to apply AI to advance the United Nations' Sustainable Development Goals (SDGs) for social good.”

    From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [78]

  • Human-compatible, human-centric approaches, AI for social good, and human computation or interactive machine learning are computing paradigms aligned with the human agency and oversight requirement.

    “For the preservation of human rights, humancompatible [20], human-centric approaches [14,21,22], AI for social good [7,23] and human computation or interactive machine learning [24] are computing paradigms aligned with this requirement.”

    From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [23]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 28

  1. [4]Chiu, C. et al. State-of-the-art speech recognition with sequence-to-sequence models. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 4774–4778 (2018).
  2. [5]Bojarski, M. et al. End to end learning for self-driving cars. Preprint at https://arxiv.org/abs/1604.07316 (2016). · arXiv
  3. [6]Yu K-H Beam A Kohane IS Artificial intelligence in healthcare Nat. Biomed. Eng. 2018 2 719 731 10.1038/s41551-018-0305-z 31015651 · DOI · Paper page
  4. [8]Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation, Preprint at https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf (2018).
  5. [12]Luck, M. et al. AI for Social Good NeurIPS 2018 Workshop. https://aiforsocialgood.github.io/2018/index.htm (2018).
  6. [13]Fang, F. et al. AI for Social Good NeurIPS 2019 Workshop. https://aiforsocialgood.github.io/neurips2019/ (2019).
  7. [14]Luck, M. et al. AI for Social Good ICML 2019 Workshop. https://aiforsocialgood.github.io/icml2019/ (2019).
  8. [15]Luck, M. et al. AI for Social Good ICLR 2019 Workshop. https://aiforsocialgood.github.io/iclr2019/ (2019).
  9. [18]Data science for social impact. https://www.mastercardcenter.org/press-releases/center-for-inclusive-growth-rockefeller-foundation-announce-data-science-project (2019).
  10. [21]Newman, N. et al. Designing and evolving an electronic agricultural marketplace in Uganda. In (ed. Zegura, E.) Proc. 1st ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 18, New York, NY, 14:1–14:11 (ACM, 2018).
  11. [22]Jean N Combining satellite imagery and machine learning to predict poverty Science 2016 353 790 794 10.1126/science.aaf7894 27540167 · DOI · Paper page
  12. [23]Cornebise, J., Worrall, D., Farfour, M. & Marin, M. Witnessing atrocities: quantifying villages destruction in Darfur with crowdsourcing and transfer learning. In Proc. AI for Social Good NeurIPS2018 Workshop, NeurIPS ’18,Montreal, Canada (2018).
  13. [24]Monteleoni C Schmidt GA McQuade S Climate informatics: accelerating discovering in climate science with machine learning Comput. Sci. Eng. 2013 15 32 40 10.1109/MCSE.2013.50 · DOI
  14. [25]Rolnick, D. et al. Tackling climate change with machine learning. Preprint at https://arxiv.org/abs/1906.05433 (2019). · arXiv
  15. [31]Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S. & Vertesi, J. Fairness and abstraction in sociotechnical systems. In (eds Chouldechova, A. & Diaz, F.) Proc. Conference on Fairness, Accountability, and Transparency, FAT* ’19, New York, NY, USA, 59–68 (ACM, 2019).
  16. [38]ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT). https://facctconference.org/ (2019). PMC10661580 37990734
  17. [43]Donner J Gandhi R Javid P Medhi I Ratan A Stages of design in technology for global development Computer 2008 41 34 41 10.1109/MC.2008.203 · DOI
  18. [45]Chouldechova, A. & Roth, A. The frontiers of fairness in machine learning, Preprint at https://arxiv.org/abs/1810.08810 (2018). · arXiv
  19. [46]Corbett-Davies, S. & Goel, S. The measure and mismeasure of fairness: a critical review of fair machine learning. Preprint at https://arxiv.org/abs/1808.00023 (2018). · arXiv
  20. [49]SageWeber, J. & Toyama, K. Remembering the past for meaningful ai-d. In Artificial Intelligence for Development, AAAI Spring Symposium Series. AAAI. 97–102 (2010).
  21. [52]Erikson SL Building data responsibility into humanitarian action Med. Anthropol. Q. 2018 32 315 339 10.1111/maq.12440 29520829 PMC6175342 · DOI
  22. [53]Hutter, F., Kotthoff, L., & Vanschoren, J. (eds) Automated Machine Learning: Methods, Systems, Challenges. Springer (2019).
  23. [55]Lawrence, N. D. Data readiness levels. Preprint at https://arxiv.org/abs/1705.02245 (2017). · arXiv
  24. [56]Raina, R., Battle, A., Lee, H., Packer, B. & Ng, A. Y. Self-taught learning: Transfer learning from unlabeled data. In (ed Ghahramani, Z.) Proc. 24th International Conference on Machine Learning, ICML ’07, New York, NY, USA, 759–766 (ACM, 2007).
  25. [57]Socher, R., Ganjoo, M., Manning, C. D., & Ng, A. in Advances in Neural Information Processing Systems Vol. 26 (eds Burges, C. J. C.), 935–943 (Curran Associates, Inc., 2013).
  26. [61]Delisle, L. et al. A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter. Preprint at https://arxiv.org/abs/1902.03093 (2019). · arXiv
  27. [67]Abbott, J. & Martinus, L. Towards neural machine translation for african languages. Preprint at https://arxiv.org/abs/1811.05467 (2018). · arXiv
  28. [68]Orife, I. et al. Masakhane - Machine Translation For Africa, Preprint at https://arxiv.org/abs/1810.08810 (2020). · arXiv

Cited by

Sorted by citations

Authors
Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. D., Teh, Y. W., Khan, M. E., Winne, R. D., Schaul, T., & Clopath, C.
Affiliations
  • Google DeepMind (United Kingdom) (GB)
  • University College London (GB)
  • University of Freiburg (DE)
  • Oxfam (GB)
  • Microsoft Research (United Kingdom) (GB)
  • University of Warwick (GB)
  • Rwanda Development Board (RW)
  • RIKEN (JP)
  • Google (Switzerland) (CH)
  • Ruhr University Bochum (DE)
  • University of Oxford (GB)
Corresponding author
Tomašev, Nenad
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-05-18
Volume
11
Issue
1
Pages
2468-2468
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-15871-z
PMC
PMC7235077
OpenAlex
W3025490408
Cited by
357
References
6
Citations by year
2026: 46 · 2025: 92 · 2024: 81 · 2023: 61 · 2022: 44 · 2021: 31 · 2020: 2
FWCI
6.70
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Greater South Information System
Funders
  • Alan Turing Institute (TU/A/000017, TU/ASG/R-SPES-107)
  • UK Research and Innovation (FP7/2007-2013)
  • European Commission (2007–2013, FP7 2007-2013, FP7/2007)
  • Engineering and Physical Sciences Research Council (EP/S001360/2, TU/A/000017, FP7/2007–2013, EP/S001360/1)
  • Japan Society for the Promotion of Science (20H05574, 17H05920, 20H05022, 19H01138, KAKENHI Grant Number JP16H06395, JP16H06395,)
  • FP7 Ideas: European Research Council (617071)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Sociology and Political Science › Innovation, Technology, and Society
Keywords
AI for social good, Sustainable Development Goals, interdisciplinary collaboration, societal impact of AI, machine learning applications, research guidelines

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review