Hacking your attention, with Luca Belli - HockeyStick #56 copertina

Hacking your attention, with Luca Belli - HockeyStick #56

Hacking your attention, with Luca Belli - HockeyStick #56

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Hello everyone!I’m Miko Pawlikowski, and in this #56 episode of The Hockey Stick Show, I sat down with Luca Belli, AI safety lead at Spring Health and author of the upcoming book Hidden Influences.Luca spent more than five years at Twitter, where he co-founded the Machine Learning Ethics, Transparency, and Accountability team. Our conversation explored something most of us interact with every day without thinking much about it: recommendation algorithms.They decide what appears in our feeds, what products we discover, which people we meet, and increasingly, how we understand what is happening in the world.How Much of Your Life Is Chosen by an Algorithm?Probably more than you think.Social media has become one of our main gateways to the internet. We use it to keep in touch with people, read the news, discover ideas, and make sense of what is happening around us.Almost everything we see there has been selected by a recommendation system.But the influence goes much further than social media.Dating apps recommend people we might want to meet. Event platforms suggest where we should go. Shopping sites decide which products are worth showing us. Those recommendations can eventually affect who we meet, what we buy, what we become interested in, and what we do offline.The technology has become so deeply embedded in everyday life that we often stop noticing it.Generative AI Is Only Part of the StoryMuch of the current debate about AI focuses on generative models.Fake images. AI-generated video. Misinformation.Luca pointed out that creating misleading content is only part of the problem.For that content to matter, somebody has to see it.And very often, the mechanism deciding who sees it is a recommender system.A fake video sitting somewhere on the internet has limited impact. A system that decides it is engaging and pushes it in front of millions of people is a different problem entirely.Recommendation algorithms have been influencing information distribution for years, long before generative AI became the centre of the conversation.The Algorithm Is Learning From You. You’re Learning From It.One of the most interesting parts of our conversation was the difficulty of separating our own preferences from the systems around us.Recommendation systems learn continuously from our behaviour.You click something. You watch a video. You follow someone. You like a post.Those actions become signals that influence what you see next.But the influence also runs in the opposite direction.An algorithm might repeatedly expose you to a topic you had never previously considered. Eventually you become interested in it.Was that interest always there, waiting to be discovered?Or did the recommendation system help create it?There isn’t a clean boundary.Luca described recommendation systems as socio-technical systems. You cannot properly study the algorithm without also studying the people interacting with it.The user changes the system, and the system changes the user.That feedback loop is part of what makes recommendation algorithms so powerful.There Is No Correct RecommendationBuilding a system that recognises whether an image contains a cat or a dog has something recommendation systems don’t: a ground truth.Most people can agree whether the image contains a cat.But what is the objectively correct recommendation for a person?There isn’t one.Something I find interesting might be completely irrelevant to you. Even my own preferences change depending on the situation.The things I want to read in the morning might be completely different from what I want after a long day at work.That creates a fundamental problem.If there is no universal definition of a good recommendation, what exactly should these systems optimise for?Clicks?Time spent?Satisfaction?Something else entirely?There is no simple gold standard against which a recommendation algorithm can be measured.And that makes questions about whether one is behaving “correctly” much harder than they initially appear.Hidden InfluencesLuca’s book Hidden Influences grew out of his work studying responsible AI and the societal impact of recommendation systems.At Twitter, he worked particularly on algorithmic amplification and the amplification of political content.After Twitter was acquired in 2022, he decided it was time to move on. The book became a way to put several years of thinking and research into one place and, as he put it, get some closure before starting the next chapter.It isn’t a manual for engineers trying to build a recommender system from scratch.Instead, Luca wants to explain what these systems do, how they interact with people, and why their social consequences can be difficult to predict.One audience he would particularly like to reach is policymakers.Regulating recommendation algorithms requires understanding what actually happens under the hood, rather than treating “the algorithm” as a mysterious black box.Moderation Gets Complicated ...
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