An Evaluation of Recommendation-Based Algorithms in the Context of Gonzalez v. Google - Adrianna Northrop (Washington University Journal of Law and Policy, 2024)
In response to the growth and development of the Internet, Congress passed the Communications Decency Act of 1996 (“CDA”). In Section 230, the CDA provides immunity for social media platforms and other online services from civil liability arising out of third-party content and allows platforms to moderate third-party content without fear of liability for failing to do so. In 2022, the Supreme Court of the United States took up Gonzalez v. Google as the Court’s first occasion to address Section 230 but ultimately left the Section 230 question for another day. Gonzalez presented the question whether social media platforms are protected under Section 230 for use of recommendation-based algorithms as they are for other, traditional editorial functions such as decisions about whether to publish, edit, or withdraw content from a platform. This Note seeks to address the arguments for and against allowing recommendation-based algorithms to be covered under Section 230, focusing on statutory construction of the CDA. In doing so, this Note analyzes recommendation-based algorithms in the context of First Amendment considerations, political misinformation, and how misinformation leads to violence and illegal activity. As a result, social media platforms and other interactive computer services should be held liable for certain harms caused by content promoted through recommendation-based algorithms. Ultimately, this Note argues that the Supreme Court should have held that recommendation-based algorithms are not protected under Section 230, as both legal precedent and policy demand such a result. Finally, it proposes a test for determining whether algorithms should be protected under Section 230, focusing on the transformative nature and the moderative purposes of the algorithm.
Keywords: Section230; GonzalezvGoogle; AlgorithmLiability; SocialMediaRegulation; FirstAmendment