Algorithmic Gender and Race Bias: a Canadian Broadcast Policy Concern?

My new article "Discoverability and Algorithmic Recommendations in Video Streaming Platforms:Algorithmic Gender and Race Bias as a Canadian Broadcast Policy Concern" with Fizza Kulvi, Faiza Hirji, Manveetha Muddaluru, Emmanuel Appiah, Leandra Greenfield, Erica Rzepeci, and Christine Quail recently came out.  

Our article examines discoverability and algorithmic recommendation systems in video streaming systems like Netflix or YouTube. We were particularly interested in looking at gender and race bias in algorithmic recommenders, and how concerns about those biases were seen:

1) by creators, policymakers, and industry members, and 

2) by parliamentarians in debates about Canada's Online Streaming Act.

We found that while algorithmic video streaming recommenders are often portrayed as neutral and as responding to users' interests by parliamentarians and video streaming platforms, our interview participants - and research on recommendation systems - are far more skeptical.

The Algorithmic Distribution of News: Policy Responses

My new edited collection with James Meese, The Algorithmic Distribution of News: Policy Responses, examines regulatory responses - and the responses of journalism organizations -  to the distribution of news by algorithms. It examines how news recommenders and aggregators like Google News and the Facebook news feed have challenged the traditional news gatekeepers and the revenue flows of news organizations.  Countries around the world have come up with a broad range of policy responses. The spectacular array of authors in this book book provide both reflections on these initial responses, as well as some food for thought about future directions.