Section 230 & Gonzalez: Algorithmic Recommendations Are Immune

Ben Sperry —  1 February 2023

In our previous post on Gonzalez v. Google LLC, which will come before the U.S. Supreme Court for oral arguments Feb. 21, Kristian Stout and I argued that, while the U.S. Justice Department (DOJ) got the general analysis right (looking to as the framework for exceptions to the general protections of Section 230), they got the application wrong (saying that algorithmic recommendations should be excepted from immunity).

Now, after reading Google’s brief, as well as the briefs of amici on their side, it is even more clear to me that:

  1. algorithmic recommendations are protected by Section 230 immunity; and
  2. creating an exception for such algorithms would severely damage the internet as we know it.

I address these points in reverse order below.

Google on the Death of the Internet Without Algorithms

The central point that Google makes throughout its brief is that a finding that Section 230’s immunity does not extend to the use of algorithmic recommendations would have potentially catastrophic implications for the internet economy. Google and amici for respondents emphasize the ubiquity of recommendation algorithms:

Recommendation algorithms are what make it possible to find the needles in humanity’s largest haystack. The result of these algorithms is unprecedented access to knowledge, from the lifesaving (“how to perform CPR”) to the mundane (“best pizza near me”). Google Search uses algorithms to recommend top search results. YouTube uses algorithms to share everything from cat videos to Heimlich-maneuver tutorials, algebra problem-solving guides, and opera performances. Services from Yelp to Etsy use algorithms to organize millions of user reviews and ratings, fueling global commerce. And individual users “like” and “share” content millions of times every day. – Brief for Respondent Google, LLC at 2.

The “recommendations” they challenge are implicit, based simply on the manner in which YouTube organizes and displays the multitude of third-party content on its site to help users identify content that is of likely interest to them. But it is impossible to operate an online service without “recommending” content in that sense, just as it is impossible to edit an anthology without “recommending” the story that comes first in the volume. Indeed, since the dawn of the internet, virtually every online service—from news, e-commerce, travel, weather, finance, politics, entertainment, cooking, and sports sites, to government, reference, and educational sites, along with search engines—has had to highlight certain content among the thousands or millions of articles, photographs, videos, reviews, or comments it hosts to help users identify what may be most relevant. Given the sheer volume of content on the internet, efforts to organize, rank, and display content in ways that are useful and attractive to users are indispensable. As a result, exposing online services to liability for the “recommendations” inherent in those organizational choices would expose them to liability for third-party content virtually all the time. – Amicus Brief for Meta Platforms at 3-4.

In other words, if Section 230 were limited in the way that the plaintiffs (and the DOJ) seek, internet platforms’ ability to offer users useful information would be strongly attenuated, if not completely impaired. The resulting legal exposure would lead inexorably to far less of the kinds of algorithmic recommendations upon which the modern internet is built.

This is, in part, why we weren’t able to fully endorse the DOJ’s brief in our previous post. The DOJ’s brief simply goes too far. It would be unreasonable to establish as a categorical rule that use of the ubiquitous auto-discovery algorithms that power so much of the internet would strip a platform of Section 230 protection. The general rule advanced by the DOJ’s brief would have detrimental and far-ranging implications.

Amici on Publishing and Section 230(f)(4)

Google and the amici also make a strong case that algorithmic recommendations are inseparable from publishing. They have a strong textual hook in Section 230(f)(4), which explicitly protects “enabling tools that… filter, screen, allow, or disallow content; pick, choose, analyze or disallow content; or transmit, receive, display, forward, cache, search, subset, organize, reorganize, or translate content.”

As the amicus brief from a group of internet-law scholars—including my International Center for Law & Economics colleagues Geoffrey Manne and Gus Hurwitz—put it:

Section 230’s text should decide this case. Section 230(c)(1) immunizes the user or provider of an “interactive computer service” from being “treated as the publisher or speaker” of information “provided by another information content provider.” And, as Section 230(f)’s definitions make clear, Congress understood the term “interactive computer service” to include services that “filter,” “screen,” “pick, choose, analyze,” “display, search, subset, organize,” or “reorganize” third-party content. Automated recommendations perform exactly those functions, and are therefore within the express scope of Section 230’s text. – Amicus Brief of Internet Law Scholars at 3-4.

In other words, Section 230 protects not just the conveyance of information, but how that information is displayed. Algorithmic recommendations are a subset of those display tools that allow users to find what they are looking for with ease. Section 230 can’t be reasonably read to exclude them.

Why This Isn’t Really (Just) a Case

This is where the DOJ’s amicus brief (and our previous analysis) misses the point. This is not strictly a case. The case actually turns on whether algorithmic recommendations are separable from publication of third-party content, rather than whether they are design choices akin to what was occurring in that case.

For instance, in our previous post, we argued that:

[T]he DOJ argument then moves onto thinner ice. The DOJ believes that the 230 liability shield in Gonzalez depends on whether an automated “recommendation” rises to the level of development or creation, as the design of filtering criteria in did.

While we thought the DOJ went too far in differentiating algorithmic recommendations from other uses of algorithms, we gave them too much credit in applying the analysis. Section 230 was meant to immunize filtering tools, so long as the information provided is from third parties. Algorithmic recommendations—like the type at issue with YouTube’s “Up Next” feature—are less like the conduct in and much more like a search engine.

The DOJ did, however, have a point regarding algorithmic tools in that they may—like any other tool a platform might use—be employed in a way that transforms the automated promotion into a direct endorsement or original publication. For instance, it’s possible to use algorithms to intentionally amplify certain kinds of content in such a way as to cultivate more of that content.

That’s, after all, what was at the heart of The site was designed to elicit responses from users that violated the law. Algorithms can do that, but as we observed previously, and as the many amici in Gonzalez observe, there is nothing inherent to the operation of algorithms that match users with content that makes their use categorically incompatible with Section 230’s protections.


After looking at the textual and policy arguments forwarded by both sides in Gonzalez, it appears that Google and amici for respondents have the better of it. As several amici argued, to the extent there are good reasons to reform Section 230, Congress should take the lead. The Supreme Court shouldn’t take this case as an opportunity to significantly change the consensus of the appellate courts on the broad protections of Section 230 immunity.