20 min

The dark side of AI: recommend and manipulate (Ep. 90) Data Science at Home

    • Technology

In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates, like environmental issues, abortion issues and so on. In simple terms, someone who is pro-environment, pro-abortion, left-leaning, is also supportive of the Black Lives Matter movement, and viceversa.
F: Ok, this seems to make sense, right? But… I suspect there is more to this story?
So far, yes…. What they did not expect to find, though, was a pervasive network of Russian accounts participating in the debate, which turned out to be orchestrated by the Internet Research Agency, the not-so-secret Russian secret service agency of internet black ops. The same connected with the US election and Brexit referendum, allegedly. 
F: Are we talking about actual spies? Where are you going with this?
Basically, the Russian accounts (part of them human and part of them bots) were infiltrating all aspects of the debate, both on the left and on the right side, and always taking the most extreme stances on any particular aspect of the debate. The aim was to radicalise the conversation, to make it more and more extreme, in a tactic of divide-and-conquer: turn the population against itself in an online civil war, push for policies that normally would be considered too extreme (for instance, give tanks to the police to control riots, force a curfew, try to ban Muslims from your country). Chaos and unrest have repercussions on international trade and relations, and can align to foreign interests.
F: It seems like a pretty indirect and convoluted way of influencing a foreign power…
You might think so, but you are forgetting social media. This sort of operation is directly exploiting a core feature of internet social media platforms. And that feature, I am afraid, is recommender systems.
F: Whoa. Let’s take a step back. Let’s recap the general features of recommender systems, so we are on the same page. 
The main purpose of recommender systems is to recommend people the same items similar people show an interest in.Let’s think about books and readers. The general idea is to find a way to predict the best book to the best reader. Amazon is doing it, Netflix is doing it, probably the bookstore down the road does that too, just on a smaller scale.Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors.
The major issue of recommender systems is in their validation. Even though validation occurs in a way that is similar to many machine learning methods, one should recommend a set of items first (in production) and measure the efficacy of such a recommendation. But, recommending is already altering the entire scenario, a bit in the flavour of the Heisenberg principle of uncertainty. 
F: In the attention economy, the business model is to monetise the time the user spends on a platform, by showing them ads. Recommender systems are crucial for this purpose.Chiara, you are saying that these algorithms have effects that are problematic?
As you say, recommender systems exist because the business model of social media platforms is to monetise attention. The most effective way to keep users’ attention is to show them stuff they could show an interest in.In order to do that, one must segment the audience to find the best content for each user. But then, for each user, how do you keep them engaged, and make them consume more content? 
F: You’re going to say the word “filter bubble” very soon.
Spot on. To keep

In 2017 a research group at the University of Washington did a study on the Black Lives Matter movement on Twitter. They constructed what they call a “shared audience graph” to analyse the different groups of audiences participating in the debate, and found an alignment of the groups with the political left and political right, as well as clear alignments with groups participating in other debates, like environmental issues, abortion issues and so on. In simple terms, someone who is pro-environment, pro-abortion, left-leaning, is also supportive of the Black Lives Matter movement, and viceversa.
F: Ok, this seems to make sense, right? But… I suspect there is more to this story?
So far, yes…. What they did not expect to find, though, was a pervasive network of Russian accounts participating in the debate, which turned out to be orchestrated by the Internet Research Agency, the not-so-secret Russian secret service agency of internet black ops. The same connected with the US election and Brexit referendum, allegedly. 
F: Are we talking about actual spies? Where are you going with this?
Basically, the Russian accounts (part of them human and part of them bots) were infiltrating all aspects of the debate, both on the left and on the right side, and always taking the most extreme stances on any particular aspect of the debate. The aim was to radicalise the conversation, to make it more and more extreme, in a tactic of divide-and-conquer: turn the population against itself in an online civil war, push for policies that normally would be considered too extreme (for instance, give tanks to the police to control riots, force a curfew, try to ban Muslims from your country). Chaos and unrest have repercussions on international trade and relations, and can align to foreign interests.
F: It seems like a pretty indirect and convoluted way of influencing a foreign power…
You might think so, but you are forgetting social media. This sort of operation is directly exploiting a core feature of internet social media platforms. And that feature, I am afraid, is recommender systems.
F: Whoa. Let’s take a step back. Let’s recap the general features of recommender systems, so we are on the same page. 
The main purpose of recommender systems is to recommend people the same items similar people show an interest in.Let’s think about books and readers. The general idea is to find a way to predict the best book to the best reader. Amazon is doing it, Netflix is doing it, probably the bookstore down the road does that too, just on a smaller scale.Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors.
The major issue of recommender systems is in their validation. Even though validation occurs in a way that is similar to many machine learning methods, one should recommend a set of items first (in production) and measure the efficacy of such a recommendation. But, recommending is already altering the entire scenario, a bit in the flavour of the Heisenberg principle of uncertainty. 
F: In the attention economy, the business model is to monetise the time the user spends on a platform, by showing them ads. Recommender systems are crucial for this purpose.Chiara, you are saying that these algorithms have effects that are problematic?
As you say, recommender systems exist because the business model of social media platforms is to monetise attention. The most effective way to keep users’ attention is to show them stuff they could show an interest in.In order to do that, one must segment the audience to find the best content for each user. But then, for each user, how do you keep them engaged, and make them consume more content? 
F: You’re going to say the word “filter bubble” very soon.
Spot on. To keep

20 min

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