In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time when their impact continues to grow, there are many settings where these algorithms are far from transparent. There is growing awareness about the vast amounts of data companies are collecting on their users and customers, and people are starting to demand control over their data. A similar conversation is starting to happen about algorithms—users are wanting more control over what these models optimize for and an understanding of how they work.
I first came across Chaslot through a series of articles about the power and impact of YouTube on politics and society. Many of the articles I read relied on data and analysis supplied by Chaslot. We talked about his work trying to decipher how YouTube’s recommendation system works, filter bubbles, transparency in machine learning, and data privacy.
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Here are some highlights from our conversation:
Why YouTube’s impact is less understood
My theory why people completely overlooked YouTube is because on Facebook and Twitter, if one of your friends posts something strange, you’ll see it. Even if you have 1,000 friends, if one of them posts something really disturbing, you see it, so you’re more aware of the problem. Whereas on YouTube, some people binge watch some very weird things that could be propaganda, but we won’t know about it because we don’t see what other people see. So, YouTube is like a TV channel that doesn’t show the same thing to everybody and when you ask YouTube, “What did you show to other people?” YouTube says, ‘I don’t know, I don’t remember, I don’t want to tell you.’
Downsides of optimizing only for watch time
When I was working on the YouTube algorithm and our goal was to optimize watch time, we were trying to make sure that the algorithm kept people online the longest. But what I realized was that we were so focused on this target of watch time that we were forgetting a lot of important things and we were seeing some very strange behavior of the algorithm. Each time we were seeing this strange behavior, we just blamed it on the user. It shows violent videos; it must be because users are violent, so it’s not our fault; the algorithm is just a mirror of human society. But if I believe the algorithm is a mirror of human society, I think it’s also not a flat mirror; it’s a mirror that emphasizes some aspects of life and makes some other aspects overlooked.
… The algorithm that is behind YouTube and the Facebook news feeds are very complex, deep learning systems that will take a lot into account, including user sessions, what they’ve watched. It will try to find the right content to show to users to get them to stay online the longest and interact as much as possible with the content. So, this can seem neutral at first, but it might not be neutral. For instance, if you have content that says ‘The media is lying,’ whether it’s on Facebook or on YouTube, what will happen is that this content will naturally, if it manages to convince the user that the media is lying, the content will be very efficient at keeping the user online because the user won’t go to other media and will spend more time on YouTube and more time on Facebook.
… In my personal opinion, the current goal of maximizing watch time means that any content that is really good at captivating your attention for a long time will perform really well. This means extreme content will actually perform really well. But say you had another goal—for instance, the goal to maximize likes and dislikes, or another system of rating like when you would be asked some question like, ‘Did you enjoy this video? Was it helping you in your life?’ Then this kind of extreme content will not perform as well. So, there are many other options, and it’s not that YouTube is failing at exploring these other options; it’s that they don’t even try.
Related resources:
- “We need to build machine learning tools to augment machine learning engineers”
- “Defining responsible data practices”: Natalie Evans Harris discusses the community principles on ethical data practices (CPEDP), a code of ethics for data collection, sharing, and utilization.
- “Inclusivity for the greater good”: Ajey Gore explains why GO-JEK is focusing its attention beyond urban Indonesia to help people across the country’s rural areas.
- “Ethics in data project design: It’s about planning”
- “On computational ethics”
- Ethics of Big Data: Doug Patterson and Kord Davis on balancing risk and innovation.
