Community-Based Data Justice: A Model for Data Collection in Informal Urban Settlements
Denisse Albornoz, Katherine Reilly & Marieliv Flores
Abstract
Non-governmental organizations (NGOs) have become important curators of data from informal urban settlements. Given the absence of these communities in public datasets, they work to take informal settlements from a state of invisibility and injustice to one of visibility and justice, in and through data. The working premise of these NGOs is that by producing data about informal settlements, data can act as a “currency” through which individuals can access different forms of justice. However, the literature that studies datafication in marginalized urban communities shows this is not always the case. Data scholars have pointed out that datafication implies a series of risks, as well as new forms of exclusion and inequality for vulnerable populations and minorities. This paper studies, through the analysis of interviews of residents of informal settlements in Lima, Peru, whether intensive data collection in informal settlements is considered a process to access justice by vulnerable communities.
The study concludes that in the short term, datafication does not give access to justice to vulnerable communities but in fact, deepens or reproduces instances of oppression by reinforcing the perception of their lack of knowledge, lack of capabilities or lack of authority to use data to lead their own development. However, participating in the process of datafication sparked an interest among community leaders about the different ways in which data could be used to further their capabilities, mobilize collective action and address their development needs. Community leaders are interested and willing to use data in constructive ways to collaborate with diverse actors and transform their conditions. However, this interest, power and potential needs to be activated through capability development and the cooperation of data partners willing to invest their resources to provide this training. The paper closes with a list of recommendations, suggested by community leaders from informal settlements, on how to build a community-based data collection model that redresses harms and individual forms of injustice from previous experiences.
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