Discoverability and Algorithmic Recommendations in Video Streaming Platforms: Exploring Algorithmic Gender and Race Bias as a Canadian Broadcast Policy Concern.

This article examines concerns about algorithmic gender and race bias in video streaming platforms' recommendation systems as a Canadian broadcast policy issue, particularly in light of the 2023 Online Streaming Act empowering the Canadian Radio-television and Telecommunications Commission (CRTC) to require streaming services to ensure the discoverability of Canadian content. Through interviews with Canadian screen industry stakeholders—including advocacy organizations, creators, and industry leaders—the study reveals that current policy debates focus mainly on promoting Canadian, Francophone, and Indigenous content, with little attention to how algorithms may perpetuate narrow content promotion and fail to adequately "see" or recommend works by racialized women and other marginalized groups. Participants highlighted the lack of access to streaming platform data as a major barrier to identifying and addressing algorithmic biases, and suggested roles for platforms and regulators in improving transparency, promoting diverse content through recommendations, granting users more autonomy, and considering quota systems that reflect intersecting identity categories. The article notes a disconnect between these concerns and parliamentary discussions, which often portray recommendation algorithms as neutral and market-driven, underscoring the need for further scrutiny and data access to address equity issues in Canadian broadcasting policy.