Are we promoting Canadian content through deep-learning algorithms?

Algorithms are shaping our world, in video content and social media

Share this video

Day: May 11
Time: 9:00AM - 10:15AM

In this age of abundance, algorithms are emerging as the great enabler and tool for discovery. Searching for the right movie, piece of music or headline news is a mathematical equation away – one that is already sophisticated enough to feed off your mood, activity and time of day. This session explores both the opportunity and the pitfalls of algorithms in helping us find the needles in the proverbial haystack.

Video summary

How do algorithms shape our world? It turns out that you are actually training the algorithm…or is it training you? Apparently, with the current advances in machine learning, both are true. The best deep-learning algorithms now combine content filtering and collaborative filtering for a hybrid method that provides a massive advantage in companies’ understanding of the consumer. For instance, when you consistently click on the second item in a list, rather than the first one, say, you’re training the algorithm in the process called collaborative filtering. In this video, information and communication technology experts discuss the intersections between humans, algorithms, and regulations. The relationship between algorithms and policy – such as Canadian content policy – is now dependent on ‘filter bubbles’ in the social network. Is there an increasing loss of diversity as a result of the ownership of content-distribution platforms? Maybe there’s a role for the algorithm, and a role for government and corporations in promoting Canadian content.

Experts in this video

Assistant Professor, Communication Studies Concordia University


Computer science professor, Université du Québec

Christopher Berry

Senior Manager, Product Intelligence

Video transcript excerpts

“[We need] to be specific about the influence of algorithms…and try to understand how humans and algorithms relate both in terms of teams working to design what’s trending, while also simultaneously how humans and public interact with these algorithms…What exactly is the role of algorithms in an age of discoverability?”

“When [we’re] talking about algorithms, there are a lot of different approaches. It’s not just one-size-fits-all. Each has their own flavor, and I think that has a big impact on the kinds of content that we’re going to be receiving, taking in in the future.”

“You ended up getting a fascinating…fish-net effect, where aggregate human behavior was actually shaping the statistics and shaping which books were actually going to be seen and presented [in a recommender system]. It’s a two-way street…there’s the intent of the data scientist and the computer scientist in constructing the algorithm, and then there’s what all of you folks do to them…Some of the best algorithms learn, and they learn [from] measurements that you’re putting in. When you choose to click on the second item in a list, as opposed to the third item or the first item, you’re training it.”

“A lot of these [algorithms] are working from correlation rather than causation. Just looking at a bunch of data, you can find weird connections, like three days before tornado hits, Target sells more strawberry Pop Tarts.”

“We’re still stuck in the bubble, except that instead of [the trend] being decided by the news agencies, it’s algorithms who do it.”

“Is there a role for the government or big corporation in Canada to play in promoting Canadian content? I think this is a very important question, and…if we leave it up to algorithms, we may not like the result in the end.”

View full transcript