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Breaking Algorithmic Immunity: Why Section 230 Immunity May Not Extend to Recommendation Algorithms

Abstract: In the mid-1990s, internet experiences were underwhelming by today’s standards, despite the breakthrough technologies at their core. When a person logged on to the internet, they were met with a static experience. No matter who you were, where you were, or how you accessed a particular website, it rendered a consistent page. Today, internet experiences are personalized, dynamic, and vast—a far cry from the digital landscape of just a few decades ago. While today’s internet is unrecognizable compared with its early predecessors, many of its governing laws remain materially unaltered. In particular, section 230 of the Communications Act, which passed in 1996, remains a critical element of the bedrock upon which the internet has flourished.

While the words of section 230’s primary provisions remain unchanged, courts’ applications have somewhat modernized to keep pace with technology. However, recommendation algorithms pose an especially tricky challenge for section 230 analyses. Initially, courts extended section 230 immunity to internet platforms for algorithmic recommendations of third-party information, but a growing cohort of circuit judges are questioning whether that treatment stretches the statute too far. Although the United States Supreme Court had an opportunity to weigh in through Gonzalez v. Google, that case’s disposition ultimately left the issue open. This Comment dives deep into the current section 230 doctrine and examines its application to recommendation algorithms. While multiple theories have emerged that could successfully limit section 230 immunity’s reach to recommendation algorithms, each will have distinct implications for the future of consumer technology. Regardless, there are multiple strategies that can feasibly negate section 230 immunity when the defendant has used recommendation algorithms.


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