As a woman of colour interested in the intersections of data and racial justice, I’ve been following and inspired by a number of relevant projects in the USA, including MIT’s Data 4 Black Lives conference, AAPI Data, and the Mapping Police Violence project.
In the age of algorithmic decision making, big data poses significant dangers to racialized communities, often exacerbating existing inequities through tools like predictive-policing and algorithmic sentencing. But as some of the above projects demonstrate, data can also play an important role in painting a picture of the scale, reach, and nature of injustice.
Of course, for many communities of colour, acts of racial discrimination aren’t simply numbers and statistics: they are painful, everyday lived experiences. Unfortunately, more often than not, these lived experiences are (frustratingly) considered “not enough” evidence—and for this reason, data can be a powerful tool for communicating the institutionalized nature of racial inequities to the broader public and making a case for change.
Data for Racial Equity in Canada
Up until now, many relevant datasets in Canada have been largely missing or incomplete. Race-based data are often simply not collected; for instance, Indigenous health has been described as “data-deficient” due to a lack of adequate funding. Sometimes, racial data collection is even actively suppressed, such as in the case of Canada’s police departments—in 2009, a study revealed that 20% of Canadian police services refused to report racial data as official policy. Although the realities of racial profiling and over-policing are all too familiar for black and Indigenous individuals—they are grossly overrepresented in the Canadian prison system—it is challenging to keep police departments accountable when they actively refuse to collect data that would demonstrate such.
Ontario’s Anti-Racism Data Standards
Since starting my role at Powered by Data a few months ago, I’ve been learning more about the work being done in Canada to address the need for better data as a stepping stone towards racial justice. As part of Ontario’s 3-year anti-racism strategy and the Anti-Racism Act (2017); the province recently launched Ontario’s Anti-Racism Data Standards. These will be standardized methods for public sector organizations working in the areas of child welfare, education, and justice on collecting disaggregated race-based data on the people they serve (though health appears to be missing from the service categories outlined). The intention behind this data collection is to “identify and monitor racial disparities in order to eliminate systemic racism and advance racial equity”. Public sector organizations will be required to publish de-identified data as open data.
These standards were developed in close collaboration with the Information and Privacy Commissioner’s Office and the Ontario Human Rights Commission.
One of the networks that has pushed for disaggregated data collection in Ontario is the Colour of Poverty-Colour of Change. They are a network of NGOs, advocacy groups, and individuals committed to creating coordinated strategies for addressing structural racial inequality in Ontario. We look forward to highlighting their data-related work in greater detail in a future Knowledge Centre post.
Of course, the conversation doesn’t—and shouldn’t—end here. Earlier this week, I attended a great panel discussion at RightsCon on the control, benefits, and consequences of data collection on marginalized communities. Panelists explored important questions around ownership of race-based data (and what happens when race-based data is in the wrong hands), and whether or not organizations will be accountable to changing practices to address racial disparities after data collection. Panelists also brought up the lack of trust that can come from communities for whom data collection has historically been a tool for surveillance and exploitation.
Are you aware of any other projects happening in Ontario—or Canada more broadly—that connect the dots between racial equity and data? Are there additional considerations and risks we should be exploring around data collection on communities at the margins? I would love to have folks join this conversation!