Social media firms claim they’re just trying to build communities and connect the world and that they need ad revenues to remain free. But nothing is really free. For them, more views mean more money and so they’ve optimized their algorithms to maximize engagement. Views are the algorithms’ “reward function” — the more views the algorithms can attract to the platform the better. When an algorithm promotes a given post and sees an upsurge of views, it will double down on the strategy, selectively timing, targeting and pushing posts in ways that it has found will stimulate further sharing, a process called reinforcement learning.
It doesn’t take an AI expert to see where this leads: provocative posts that evoke strong emotions will get more views and so the algorithm will favor them, leading to ever-increasing revenues for the platform. But social platforms aren’t the only ones who use reinforcement learning AI. As companies adopt it, leaders should look to social media companies’ problems to understand how it can lead to unintended consequences — and try to avoid making predictable mistakes.
Reinforcement Learning Agents
To understand the cause and effect cycle we see on social platforms, it’s helpful to know a bit more about how the algorithm works. This type of algorithm is called a reinforcement learning (RL) agent and while these agents’ activities are perhaps most visible in social media they are becoming increasingly common…