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.
5 Rules to Manage AI’s Unintended Consequences
Reinforcement learning can lead you places you don’t want to go.
May 21, 2021
Summary.
Companies are increasingly using “reinforcement-learning agents,” a type of AI that rapidly improves through trial and error as it single-mindedly pursues its goal, often with unintended and even dangerous consequences. The weaponization of polarizing content on social media platforms is an extreme example of what can happen when RL agents aren’t properly constrained. To prevent their RL agents from causing harm, leaders should abide by five rules as they integrate this AI into their strategy execution.