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.

中文翻译：

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人道主义数据正义：尼泊尔地震后公民技术的结构性数据正义视角

随着灾难越来越多地被数据化，灾难背景下的社会正义也越来越受到数据的束缚。数据正义镜头揭示了数据项目与社会正义之间的相互联系。本文将灾害背景下的社会不公正视为韧性和风险方面的结构性不平等。第一个是指人们预防，准备，响应灾害并从灾难中恢复的能力，第二个是指人们面临易受伤害的危险的可能性。数据正义的镜头提请人们注意这些结构性不平等塑造人道主义数据项目的方式。它还显示了此类项目如何影响社会正义成果。本文展示了如何将这种镜头应用于具体的灾难环境，并为数据项目的设计人员和项目经理带来了深刻的见解。本文采用定性案例研究方法来分析尼泊尔地震后的两个地方主导的公民技术项目。这些渐进的举措旨在使受灾人员在灾难响应中发挥作用和发声，并为响应和恢复工作做出了宝贵的贡献。但是，由于在社会和数字不平等现象明显的背景下推出这些产品，它们最终仍主要使那些相对较有弹性且风险较小的人受益。本文解释了为什么会发生这种情况，并通过建议与本地先进的数字精英进行关键和战略合作以在灾难环境中实现更大的数据公正性而得出结论。