Data justice is a set of ideas that addresses the way in which people are represented and/or harmed through their data being made visible to others, or conversely, excluded from the public eye, sites of power, and the process of decision-making. It confronts and challenges structural biases in the ways that we think about, collect, steward, and use data.
Data are not and have never been neutral; the ways that we collect, represent, steward, and hide or make visible data about people and communities reflects bias. Critical media scholars have written about this from a variety of perspectives. Safiya Noble explores the ways that biased training datasets reinforce racism in search engine results in her book Algorithms of Oppression (2018), and Catherine D'Ignazio and Lauren Klein discuss the invisibility of missing and murdered women in Mexico because of the government's refusal to collect data on them in Data Feminism (2020).
The UN Women's ad campaign "Women Should" from 2013 highlights the sexism of search engine results, which Safiya Noble points out are underpinned by biased training data in "Algorithms of Oppression". Source: UN Women.
Several scholars and communities have ennumerated principles of data justice that can help us explore and challenge where power is held within systems that collect and hold data, and bring the rights of people and communities to the forefront.
Linnet Taylor defines data justice as "fairness in the way people are made visible, represented, and treated as a result of their production of digital data" (Taylor, 2017). She identifies three pillars of data justice:
The Coalition of Communities of Color addresses the ways that power imbalances in data practices have harmed entire communities, defining data justice as an approach that intentionally "redresses ways of collecting and disseminating data that have invisibilized and harmed historically marginalized communities" (Research and Data Justice, n.d.). They too identify three pillars of data justice, stating that data justice should:
As researchers and scholars, we have a responsibility to and an impact on the communities we conduct research in, with, and about. Our collection, usage, and storage of data about them affects the way that they are represented in the scholarly record and how policymakers enact laws that affect them at all levels of government. This guide will examine the ways that structural imbalances in data practices are affecting research and education, and suggest ways to intentionally practice data justice principles in one's own research.
Coalition for Communities of Color. (n.d.). Research and Data Justice. https://www.coalitioncommunitiescolor.org/-why-research-data-justice
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 2053951717736335.
Noble, S. U. (2018). Algorithms of oppression : how search engines reinforce racism. New York University Press.
This guide uses content and formatting adapted from the University of British Columbia Library's "Citation Justice" guide and the University of Maryland University Libraries "Diversity, Equity, and Inclusion in Research" guide.
Big data is increasingly becoming a topic of research and is being used in almost every discipline. Lindebaum, Moser, and Islam discuss the ways in which borrowing corporate data hurts theorizing in management research, while Yu and Fang celebrate the accessibility of modeling data for urban studies. However, data technologies are also making their way onto campuses and into the lives of student communities in perhaps unwelcome ways.
Consider, for example, how some instructors have been compelled to adopt surveillance software that sacrifices privacy in the name of academic integrity, using the exam proctoring software Proctorio in accordance with university or departmental policy. Proctorio is a remote-testing software that claims to reduce cheating, but invades privacy, allows for discrimination, and reduces accessibility. It's been pulled from several universities, including UIUC, in response to student outrage.
In another bizarre example, facial recognition software was discovered installed on a vending machine on the University of Waterloo's campus, monitoring and capturing students' data without their knowledge. In this case, no one using the vending machine, or even walking by, knew until a student noticed a revealing error message.
We should be aware of the impact of datafication - the rampant collection of data that aims to transform aspects of our lives into quantifiable units that can be measured and sold- on our scholarly lives. The collection of data by and in the educational sphere can give us new topics to study and increase our efficiency, but also quantifies irreducible aspects of our lives and can lead to generalizations, unfair biases, and in the worst cases, outright discrimination.
The very first step to incorporating equity into your data and the way you interact with it is to acknowledge that data are not objective or neutral. From collection to use and reuse, every step is filtered through each researchers' preconceived ideas and notions about what should be counted and how it should be counted. The following questions and considerations from UMD's Diversity, Equity, and Inclusion in Research Guide will help you think through the ways you interact with data throughout the research process.
Think about consent and ownership of data early to conduct research responsibly- your research participants will thank you!