Skip to main content
SpringerLink
Log in

A Framework for Building pro-bono Data for Good Projects

  • Conference paper
  • First Online:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1525))

Abstract

Initiatives relying on data science for social good - non-commercial projects that deliver socially beneficial outcomes - have been on the rise in the last years. The area of Data for Good has several specific challenges, one of which is the definition of a formal framework for the design, conception, prioritization, development and impact measurement of such applications. All over the world, volunteers are organized in local/regional initiatives that provide voluntary support to social good organizations in the development of Data for Good projects. Each of these initiatives follows specific internal frameworks that are not standardized within the community, with information-sharing efforts just starting to appear. Sharing these frameworks could lead to an increase in the amount of successful data for good projects, delivering concrete value in the daily operations of social good institutions. In this paper, the framework that was created and is being followed with success at Data Science for Social Good Portugal (DSSG PT), an open community of data enthusiasts working pro-bono in Data for Good projects, is shared. This includes all processes regarding structural organization and management, communication between stakeholders, project scoping and project development that are being followed. It also presents a methodology for social impact measurement of projects and ensuring of ethical standards, such as data privacy and fairness.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cowls, J., King, T., Taddeo, M., Floridi, L.: Designing AI for social good: seven essential factors. SSRN Electron. J. (2019). https://doi.org/10.2139/ssrn.3388669, https://www.ssrn.com/abstract=3388669

  2. Cowls, J., Tsamados, A., Taddeo, M., Floridi, L.: A definition, benchmark and database of AI for social good initiatives. Nature Mach. Intell. 3(2), 111–115 (2021). https://doi.org/10.1038/s42256-021-00296-0, http://www.nature.com/articles/s42256-021-00296-0

  3. Floridi, L., et al.: AI4people-an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach. 28(4), 689–707 (2018). https://doi.org/10.1007/s11023-018-9482-5

  4. Niño, M., et al.: Data projects for “social good”: challenges and opportunities. Int. J. Human. Soc. Sci. 11(5), 1094–1104 (2017). https://publications.waset.org/10006923/data-projects-for-social-good-challenges-and-opportunities

  5. United Nations High Commissioner for Refugees: Refworld — transforming our world : the 2030 agenda for sustainable development. https://www.refworld.org/docid/57b6e3e44.html

  6. Tomašev, N., et al.: AI for social good: unlocking the opportunity for positive impact. Nature Commun. 11(1), 2468 (2020).https://doi.org/10.1038/s41467-020-15871-z, https://www.nature.com/articles/s41467-020-15871-z, bandiera_abtest: a Cc_license_type: cc_by Cg_type: Nature Research Journals Number: 1 Primary_atype: Reviews Publisher: Nature Publishing Group Subject_term: Developing world;Ethics Subject_term_id: developing-world;ethics

  7. Wirth, R., Hipp, J.: Crisp-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, vol. 1. Springer-Verlag, London, UK (2000)

    Google Scholar 

Download references

Acknowledgements

DSSG PT thanks Catarina Farinha, Daniel Rodrigues and Marília Ferreira da Cunha for their extensive review and constructive criticism of this document. DSSG PT also acknowledges Helena Margarida Faria for the work made in the design of the figures.

Author information

Authors and Affiliations

  1. DSSG - Portuguese Association of Data Science for Social Good, Rua da Holanda 1, Carcavelos, Portugal

    Miguel José Monteiro & Paulo Maia

Authors
  1. Miguel José Monteiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Maia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel José Monteiro .

Editor information

Editors and Affiliations

  1. IKIM, Ruhr-University Bochum, Bochum, Germany

    Michael Kamp

  2. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  3. University of Namur, Namur, Belgium

    Adrien Bibal

  4. University of Rennes 1, Rennes, France

    Tassadit Bouadi

  5. University of Namur, Namur, Belgium

    Benoît Frénay

  6. Inria, Rennes, France

    Luis Galárraga

  7. University of Antwerp, Antwerp, Belgium

    José Oramas

  8. Ruhr University Bochum, Bochum, Germany

    Linara Adilova

  9. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  10. Ghent University, Ghent, Belgium

    Bo Kang

  11. Université Jean Monnet, Saint-Etienne cedex 2, France

    Christine Largeron

  12. Ghent University, Gent, Belgium

    Jefrey Lijffijt

  13. Telecom Paris, Paris, France

    Tiphaine Viard

  14. University of Bonn, Bonn, Germany

    Pascal Welke

  15. Norwegian Univesity of Science and Technology, Trondheim, Norway

    Massimiliano Ruocco

  16. BI Norwegian Business School, Oslo, Norway

    Erlend Aune

  17. University of Pisa, Pisa, Italy

    Claudio Gallicchio

  18. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  19. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  20. Xilinx Research, Dublin, Ireland

    Michaela Blott

  21. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  22. Heidelberg University, Heidelberg, Germany

    Günther Schindler

  23. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  24. University of Pisa, Pisa, Italy

    Anna Monreale

  25. ISTI-CNR, Pisa, Italy

    Salvatore Rinzivillo

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. Freie Universität Berlin, Berlin, Germany

    Eirini Ntoutsi

  28. Eindhoven University of Technology, Eindhoven, The Netherlands

    Mykola Pechenizkiy

  29. Leibniz University Hannover, Hannover, Germany

    Bodo Rosenhahn

  30. University of Sussex, Brighton, UK

    Christopher Buckley

  31. University of Chieti-Pescara, Chieti, Italy

    Daniela Cialfi

  32. Radboud University Nijmegen, Nijmegen, The Netherlands

    Pablo Lanillos

  33. McGill University, Montreal, Canada

    Maxwell Ramstead

  34. Ghent University, Ghent, Belgium

    Tim Verbelen

  35. University of Lisbon, Lisboa, Portugal

    Pedro M. Ferreira

  36. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  37. Universita di Bari Aldo Moro, Bari, Italy

    Donato Malerba

  38. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  39. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

  40. Harbin Institute of Technology, Harbin, China

    M. Saqib Nawaz

  41. University of Córdoba, Córdoba, Spain

    Sebastian Ventura

  42. Peking University, Beijing, China

    Meng Sun

  43. Noah's Ark Lab, Huawei, Beijing, China

    Min Zhou

  44. UniCredit, Milan, Italy

    Valerio Bitetta

  45. UniCredit, Rome, Italy

    Ilaria Bordino

  46. UniCredit, Milan, Italy

    Andrea Ferretti

  47. Unicredit, Rome, Italy

    Francesco Gullo

  48. ENEA Headquarters, Portici, Italy

    Giovanni Ponti

  49. Unicredit, Rome, Italy

    Lorenzo Severini

  50. University of Porto, Porto, Portugal

    Rita Ribeiro

  51. University of Porto, Porto, Portugal

    João Gama

  52. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  53. Northwestern University, Chicago, IL, USA

    Lee Cooper

  54. PD Personalised Healthcare, Basel, Switzerland

    Naghmeh Ghazaleh

  55. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  56. ETH Zurich, Basel, Switzerland

    Damian Roqueiro

  57. F. Hoffmann–La Roche Ltd, Basel, Switzerland

    Diego Saldana Miranda

  58. Novartis Pharma AG, Basel, Switzerland

    Konstantinos Sechidis

  59. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Monteiro, M.J., Maia, P. (2021). A Framework for Building pro-bono Data for Good Projects. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93733-1_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93732-4

  • Online ISBN: 978-3-030-93733-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation