When we speak online—when we share a thought, write an essay, post a photo or video—who will hear us? The answer is determined in large part by algorithms. In computer science, the algorithms driving social media are called recommender systems. These algorithms are the engine that makes Facebook and YouTube what they are, with TikTok more recently showing the power of an almost purely algorithm-driven platform.
In debates about the effects of social media, discussion of algorithms tends to be superficial. They are often assumed to be black boxes that are too complicated to understand. This is unfortunate. In fact, there is a lot that is known about how these algorithms operate. But this knowledge is not yet broadly accessible.
I think a broader understanding of recommendation algorithms is sorely needed. Policymakers and legal scholars must understand these algorithms so that they can sharpen their thinking on platform governance; journalists must understand them so that they can explain them to readers and better hold platforms accountable; technologists must understand them so that the platforms of tomorrow may be better than the ones we have; researchers must understand them so that they can get at the intricate interplay between algorithms and human behavior. Content creators would also benefit from understanding them so that they can better navigate the new landscape of algorithmic distribution. More generally, anyone concerned about the impact of algorithmic platforms on themselves or on society may find this essay of interest.
Source: Understanding Social Media Recommendation Algorithms | Knight First Amendment Institute