Module 4 • Data Justice

Authored by Caroline Kuhn with the contribution from Flor Serale

In this module, we explore the concept of data justice and the different interpretations of this complex idea. For that, we consider the different approaches to data justice, looking at their benefits and pitfalls. We then focus on Taylor’s (2017) framework for data technologies’ design and governance. The framework is a capability (Sen, 2009) and freedom-based approach that examines how data technologies influence the kind of lives that people deem valuable to live. As a complement to the notion of data justice, which is mainly a conceptual one, both, data feminism and seven inequities held in power are going to be described as actionable analytical tools to address issues of data justice when working with research in the classroom. In addition, we present the interactive Data Ethics Canvas, from the Open Data Institute.

The entire module can be downloaded in PDF by clicking on the DOI button below

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Learning Outcomes

  1. Understanding what data justice is and its different interpretations
  2. Understanding different frameworks for data justice
  3. Gaining the skills and knowledge to make the conceptual ideas about data justice actionable in teaching practice

Introductory Media

Sasha Constanza-Chock, Catherine D’Ignazio, and Jaleesa Trapp


Podcast with Sasha Constanza

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  • Design justice an approach to design that is led by marginalized communities and that aims explicitly to challenge, rather than reproduce, structural inequalities
  • Data Justice the consideration of fairness in the way people are made visible (recognised), represented and treated as a result of their production of digital data
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Recommended Readings

  1. Barassi, V. (2018). The child as datafied citizen: Critical questions on data justice in family life
  2. Constanza-Chock, S. (2020) Design Justice
  3. Dencik, L., Hintz, A., Redden, J., and Treré, E. (2019). Exploring data justice: conceptions, applications and directions. Information, Communication and Society, Vol. 22 (7).
  4. Dencik, L., Hintz, A., and Cable, J. (2016). Towards data justice? The ambiguity of anti-surveillance resistance in political activism. Big Data & Society, 3(2), 1–12. doi: 10.1177/2053951716679678
  5. Heeks, R. (2017). A structural model and manifesto for data justice for international development. Development Informatics Working Paper Series, No. 69
  6. Heeks, R. and Shekhar, S. (2019). Datafication, development and marginalised urban communities: An applied data justice framework. Information, Communication & Society, 22(7), 992-1011.
  7. Heeks, R., & Renken, J. (2016). Data Justice For Development: What Would It Mean?(Development Informatics Working Paper Series No. 63). Manchester
  8. Hoffmann, A. L. (2019). Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society, 22(7), 900-915
  9. Johnson, J. (2014). From open data to information justice. Ethics and Information Technology. Vol 16
  10. Masiero, S., & Das, S. (2019). Datafying anti-poverty programmes: Implications for data justice. Information, Communication & Society, 22(7), 916-933.
  11. Milan, S. and van der Velden, L. (2016). The Alternative Epistemologies of Data Activism. Digital Culture & Society, special issue 'The Politics of Big Data' (2016)
  12. Sen, A. (2005). Human Rights and Capabilities. Journal of Human Development, Vol 6(2)
  13. Taylor, L. (2017). What Is Data Justice? The Case for Connecting Digital Rights and Freedoms Globally. DOI: 10.1177/2053951716679678
  14. Taylor, L. (2016). Safety in numbers? Group privacy and big data analytics in the developing world. In Group Privacy: new challenges of data technologies. Dordrecht: Springer
  15. Taylor, L. (2016). The Ethics of Big Data as a Public Good: Which Public? Whose Good?
  16. Raymond, N.A. (2016). Beyond “Do No Harm” and Individual Consent: Reckoning with the Emerging Ethical Challenges of Civil Society’s Use of Data. In L. Taylor, L. Floridi, & B. van der Sloot, eds. GroupPrivacy: new challenges of data technologies. Springer.
  17. Kroll, J.A. et al., (2016). Accountable Algorithms. University of Pennsylvania Law Review, 165(633), pp.633–705
  18. Floridi, L. (2014). Open Data, data protection, and group privacy. Philosophy & Technology, 27(1), p.1
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Key Complementary Resources

  1. Building consentful technology