A Structural Model and Manifesto for Data Justice for International Development

Heeks, Richard

doi:10.2139/ssrn.3431729

Cited by 16 publications

(16 citation statements)

References 10 publications

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“…This paper builds on prior work on the social effects of datafication (e.g. Heeks, 2017; Johnson, 2014; Mulder et al., 2016; Taylor, 2017). This research shows how seemingly neutral processes, mediated by technology, reflect certain interests and priorities and, as a result, have significant social implications.…”

Section: Data Justicementioning

confidence: 98%

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Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal

Mulder

2020

Contingencies & Crisis Mgmt

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As disasters are becoming increasingly datafied, social justice in the context of disasters is increasingly bound up with data. A data justice lens reveals how data projects and social justice interlink. This paper approaches social injustice in the context of disasters as structural inequalities in terms of resilience and risk. The first refers to people's ability to prevent, prepare for, respond to and recover from disasters, and the second to the probability that people will be exposed to hazards to which they are vulnerable. A data justice lens draws attention to the ways in which these structural inequalities shape humanitarian data projects. It also shows how such projects influence social justice outcomes. This paper shows how this lens can be applied to concrete disaster settings and the insights this yields for designers and project managers of data projects. This paper uses a qualitative case study approach to analyse two locally led civic technology projects in post-earthquake Nepal. These progressive initiatives sought to give disaster-affected people a role and a voice in the disaster response-and made a valuable contribution to response and recovery efforts. However, as they were rolled out in a context marked by stark social and digital inequalities, they still ended up primarily benefitting those who were relatively more resilient and less at risk. This paper explains why this happened and concludes by recommending critical and strategic collaboration with local progressive digital elites towards greater data justice in disaster settings.

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Section: Data Justicementioning

confidence: 98%

“…A secondary focus of this paper is procedural data justice. This concept refers to fairness in how data are handled (Heeks, 2017). In terms of e‐resilience, it covers the capacity of ICTs to facilitate access to social connections and resources.…”

Section: Data Justicementioning

confidence: 99%

Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal

Mulder

2020

Contingencies & Crisis Mgmt

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“…More generally, the findings support the value of imbuing data projects with the values of data justice; ensuring their design and implementation follow principles of procedural, rights-based, instrumental, structural and distributive justice. Such principles may be found, for example, in the 'Data-Justice-for-Development Manifesto' (Heeks, 2017b): (1) Demand just and legal uses of development data.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Datafication, development and marginalised urban communities: an applied data justice framework

Heeks

Shekhar

2019

Information, Communication & Society

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The role of data within international development is rapidly expanding. However, the recency of this phenomenon means analysis has been lagging; particularly, analysis of broader impacts of real-world initiatives. Addressing this gap through a focus on data's increasing presence in urban development, this paper makes two contributions. Firstdrawing from the emerging literature on 'data justice'it presents an explicit, systematic and comprehensive new framework that can be used for analysis of datafication. Second, it applies the framework to four mapping initiatives in cities of the global South. These initiatives capture and visualise new data about marginalised communities: residents living in slums and other informal settlements about whom data has traditionally been lacking. Analysing across procedural, rights, instrumental and structural dimensions, it finds these initiatives deliver real incremental gains for their target communities. But it is external actors and wealthier communities that gain more; thus, increasing relative inequality.

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“…Our setting thus leads us to adopt a critical approach, recognizing that structural conditions, including institutions that embody those conditions, shape both the distribution of data resources and the patterns of benefits that derive from them (Johnson 2014, Heeks 2017, Heeks & Renken 2018. This approach involves examining how social, political and institutional structures condition access to information as well as services and/or resources.…”

Section: Class Languagementioning

confidence: 99%

Data Justice through the Prism of Information Politics and Resource Injustice: A Case Study from Hyderabad’s Urban Frontier

Kennedy

Sood²,

Chakraborty³

et al. 2019

SSRN Journal

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A Structural Model and Manifesto for Data Justice for International Development

Cited by 16 publications

References 10 publications

Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal

Humanitarian data justice: A structural data justice lens on civic technologies in post‐earthquake Nepal

Datafication, development and marginalised urban communities: an applied data justice framework

Data Justice through the Prism of Information Politics and Resource Injustice: A Case Study from Hyderabad’s Urban Frontier

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