The Emergence of a New Type of Generative AI
Technology companies and users alike are increasingly recognizing and embracing generative AI’s capabilities in the search space. Google quickly capitalized on its search engine prowess and unveiled an “AI Overviews” feature, which now sits atop many Google search queries. And just recently, OpenAI opened its “SearchGPT” for closed beta testing, launching it as “a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources.”
By way of illustration, when a user types a prompt into Google such as “what is the role of a lawyer?” a traditional search would simply generate a list of links to informative third-party websites with relevant information. Today, Google AI Overviews will generate a narrative answer above that list; in one search of the example prompt by the authors, it explained, “Lawyers, also known as attorneys, counselors, or counsel, are licensed professionals who advise and represent clients in legal matters. Their role is to provide legal counsel and advocacy for individuals, businesses, and government organizations.” An accompanying quasi-footnote link points readers to its source material (in this example, the ABA website).
Under the hood, both Google’s AI Overviews and OpenAI’s SearchGPT operate on similar principles: They use large language models to process and synthesize information from web searches they perform. These models are already trained on diverse Internet content, including not only reputable sources but also user-generated content and potentially unreliable information. When a query is received, the AI rapidly scans its knowledge base and retrieves relevant, up-to-date information from the Internet or relevant index of consistently updated Internet data. The AI then identifies the most relevant details and generates a response using its language model, which predicts the most probable text based on the query and all information available to it at the moment of execution. Oftentimes, the same prompt will result in a different response, which is generally true of generative AIs due to their use of stochastic optimization algorithms. These algorithms incorporate controlled randomness into the process of generating answers for problems that rely on probabilities.
However, this process is not infallible. AI models can sometimes conflate facts from different sources, misinterpret context, place too much weight on a particular source to the detriment of others, or fail to distinguish between reliable and unreliable information. These possibilities in the context of search engine functionality can lead to the propagation of misinformation, ranging from harmless misconceptions to potentially dangerous advice, all presented with the same air of authority as accurate information. Examples have cropped up since AI Overviews was unveiled by Google in July that have subsequently caused the feature’s incorporation to be partially rolled back by the company. For example, after a user complained that their car’s blinker wasn’t making an indicator noise, AI Overviews suggested the user change their blinker fluid. The problem with this advice? Blinker fluid doesn’t exist and is an inside joke among car connoisseurs. Further, these systems may occasionally “hallucinate”—generating plausible-sounding but entirely fabricated information—especially when dealing with queries outside their training data or when attempting to bridge gaps in their knowledge.
But the potential for harm goes further than bad advice about car maintenance. Generative AI search engines also risk the proliferation of defamatory content by responding to a user’s query with misleading or blatantly false characterizations of real people. By way of illustration, one anecdote reported that in response to a query about cheating in chess, Google’s AI Overviews produced a response stating that chess grandmaster Hans Niemann had admitted to cheating by using an engine or chess-playing AI when playing against the world’s top chess player, Magnus Carlsen. The problem? Niemann hadn’t admitted to cheating against Carlsen, and in fact had vociferously denied any wrongdoing, including filing a $100 million lawsuit against those who had accused him of cheating. The misleading response from Google AI Overviews was likely paraphrased from statements made by Niemann about prior online games when he was much younger. But given the predictive mechanics of Google Overviews’s AI, that context was absent from the response.
When an AI search engine promulgates inaccurate, misleading, or tortious content, who should be liable for the fallout? Google’s AI Overviews and OpenAI’s SearchGPT present unique challenges to the traditional understanding of online platforms’ roles and responsibilities. These search-integrated AI tools operate on a spectrum between traditional search engines and creative content producers. Unlike standard AI chatbots, which primarily generate responses based on preloaded training data, or traditional search engines that merely display preexisting information, these tools actively retrieve, synthesize, and produce content using information from the Internet in real time. This real-time integration of web content allows these AI search tools to create new, synthesized content from multiple sources.
As a result, these tools are increasingly taking on the role of a content creator rather than a neutral platform. This shift may have implications for the platforms’ legal liability, as it poses the question: Are providers of these AI services akin to a publisher, acting as a neutral conduit for information, or are they more analogous to an author, exercising discretion, albeit algorithmically, to generate unique content? As we explore Section 230 of the Communications Decency Act and its implications, this distinction will be crucial in understanding the potential legal challenges these new AI tools may face, and the consequences for consumers harmed by their content.
Background on Section 230
Section 230 was enacted at the beginning of the Internet’s social media era to encourage innovation by protecting interactive computer service providers from liability stemming from tortious content posted by third parties on their platforms. Under Section 230, the provider will be liable only if it “contribute[s] materially” to the alleged unlawfulness published on the platform. Section 230’s protections apply specifically when the provider “merely provides third parties with neutral tools to create web content.”
These standards have been applied to protect interactive computer service providers from liability for the publication of tortious content on their platforms. For example, in Blumenthal v. Drudge, the District Court for the District of Columbia dismissed the plaintiff’s defamation claim against AOL for its publication of an allegedly defamatory Internet article written by codefendant Matt Drudge. The Court concluded that AOL’s role in disseminating the article (providing the Internet service platform upon which the article was published) fell under Section 230’s protective umbrella. The Court explained that, in enacting the statute, Congress “opted not to hold interactive computer services liable for their failure to edit, withhold or restrict access to offensive material disseminated through their medium.” Because AOL had not authored the article, but instead only served as an intermediary for the allegedly injurious message, the Court concluded that the Internet service provider was immune under Section 230.
Neutral Intermediary or Content Contributor: Where Will Generative AI Land?
Against this backdrop, it becomes evident that generative AI search engines do not fit squarely within the existing legal framework, as they potentially occupy the roles of Internet platform and content creator simultaneously. On the one hand, a service like Google AI Overviews isn’t authoring articles in the traditional sense. But on the other hand, Google AI Overviews does more than merely provide a medium through which information can be funneled. How courts view that activity will be critical for whether generative AI search engines fit within the limitations of Section 230 immunity.
The case of Fair Housing Council of San Fernando Valley v. Roommates.com, LLC, illustrates how courts have assessed the “contributor” versus “neutral tool” dichotomy for Section 230 immunity purposes. In that case, the Court held that the website operated by the defendant had not acted as a neutral tool when the website did not “merely provide a framework that could be utilized for proper or improper purposes” but instead was directly involved in “developing the discriminatory [content].” Specifically, the defendant “designed its [website’s] search and email systems to limit [roommate] listings available to subscribers based on sex, sexual orientation and presence of children,” and “selected the criteria used to hide listings.” The Court reasoned that “a website operator who edits in a manner that contributes to the alleged illegality . . . is directly involved in the alleged illegality and thus not immune” under Section 230.
By contrast, in O’Kroley v. Fastcase, Inc., the Sixth Circuit Court of Appeals held that Section 230 barred a defamation lawsuit against Google’s presentation of its search results. In that case, the plaintiff argued that based upon the manner in which its search results were displayed, “Google did more than merely display third-party content” and was instead “responsible” for the “creation or development” of the content. The Court disagreed, noting that although Google “performed some automated editorial acts on the content, such as removing spaces and altering font,” its alterations did not “materially contribute” to the allegedly harmful content given that “Google did not add” anything to the displayed text.
Here, the functionality of Google AI Overviews functionality is arguably more akin to the Roommates platform than Google’s display of search results in O’Kroley. AI Overviews collects and curates preexisting information authored by other sources, synthesizing it to form a narrative response that is (in theory) directly responsive to the user’s query. In this regard, Google AI Overviews is analogous to an academic researcher reporting on preexisting literature in a review article. While the individual studies cited aren’t the researcher’s original work, the synthesis, analysis, and presentation of that information constitute a valuable and original contribution in the form of an academic survey. Similarly, AI Overviews’s curation and synthesis of information, while based on existing sources, results in a unique product that reflects its own algorithmically “analytical” process.
Because of the way generative AI works, even when there are independent sources of information being used to generate Google AI Overviews’s narrative answer, the response is promulgated by Google, rather than the independent sources themselves. While one might argue that AI Overviews is deciding which third-party content to include or exclude—similar to traditional search engines—AI Overviews’s role goes beyond that of an editor. In other words, Google isn’t merely a conduit for other sources of information or simply filtering which content to display; Google is serving as the speaker—essentially paraphrasing the information provided by the independent sources and sometimes doing so imperfectly. Simply put, Google’s editorial role is more substantive than merely changing the font or adding ellipses.
The following example illustrates Google AI Overviews’s editorial functionality, and how it can take seemingly innocuous content on the Internet and edit it to convey a message that could pose harm to the public. In May, Google AI Overviews went viral for suggesting its users ingest one serving of rock per day since, according to AI Overviews, rocks are “a vital source of minerals and vitamins that are important for digestive health”: “Dr. Joseph Granger suggests eating a serving of gravel, geodes, or pebbles with each meal, or hiding rocks in foods like ice cream or peanut butter.” Google’s cited source was ResFrac Corporation, a hydraulic fracturing simulation software company, which had reposted an article by The Onion, a well-known satirical news organization that publishes fictional, humorous articles parodying current events and societal issues. Pictured in the Onion article was an advisor to ResFrac, explained ResFrac in its repost.