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Paper short abstract

This paper examines ongoing debates over race-based data in clinical settings in Canada, with a focus on health equity and the uses of medical AI. It demonstrates how justice-focused legislation can play a role in addressing health inequities, making anti-racist policy, and enacting data justice.

Paper long abstract

Working to address racial bias in algorithms used in clinical settings – including diagnostic tools that may reproduce forms of systemic racism – advocates of health equity in Canada are calling for the use of datasets that are representative of the Canadian population in the training of algorithms used in an increasingly AI-integrated health care system. The collection of data on race and ethnicity in clinical settings began to take place in Canada in 2020, prompted by the deepening of existing race-based health inequities during the COVID-19 pandemic. At this time, calls for the collection of race-based data were made by health care practitioners and leaders of racialized communities, stating that “we cannot address what we cannot measure.” Without disaggregated data, it was argued, it is impossible to provide evidence of inequities that can prompt anti-racist policy and equitable clinical care. Since 2020, provinces and territories across Canada have begun to collect race-based data, along with data on languages spoken. However, these data practices remain contested, in flux, and regionally-distinct from one another, leading to significant gaps and inconsistencies in the “processing” and analysis of race-based data to make statistics at the level of the federal government to inform policymaking and clinical care. My paper discusses how race-based data collection practices in Canada are altered by Ontario’s Anti-Racism Act and British Columbia’s Anti-Racism Data Act, examining how these Acts’ placement of justice as a guidepost can play a role in addressing health inequities and enacting a form of data justice.