Tag: artificial intelligence

AI, Search, & Section 230

On May 18, the Supreme Court delivered opinions in Gonzalez v. Google and Twitter v. Taamneh, a pair of interrelated cases in which both plaintiffs sought to hold online platforms liable for hosting material meant to inspire acts of terrorism. Because the Court unanimously found in Taamneh that there was no basis in anti-terrorism law for liability (and therefore no claim for relief), it then declined to address the Section 230 question in Gonzalez, which was whether Google’s “recommendation algorithm” is sufficient to find contributory liability for the inciteful material being recommended.

Properly read, Section 230 shields OSPs from “publisher liability” but not from “distributor liability.” A distributor of allegedly harmful material may be liable when it knows, or has reason to know, the nature of the material and either affirmatively chooses to distribute it or willfully turns a blind eye to the potential harm and does nothing to stop it. Unfortunately, ever since 230 became law in 1996, the courts have generally read the law as a blanket shield for any OSP distributing any kind of material as long as it was uploaded by a user of the site and not by the site operators.

Plaintiff Gonzalez alleged that Google’s “recommendation” algorithm, designed to promote content based on the system’s interpretations of user behavior, played a crucial role in pushing ISIS propaganda toward the parties who eventually committed a mass shooting in Paris that resulted in the death of Nohemi Gonzalez. Plaintiffs argued that “targeted recommendations” are not properly shielded by Section 230, and to the extent one can read the tea leaves in oral arguments, justices as opposite as Thomas and Brown-Jackson may be sympathetic to this view.

For further reading in “Strange Bedfellows,” the amicus brief in Gonzalez filed by Senator Hawley echoes many of the same legal arguments in the brief filed by Cyber Civil Rights Initiative. Also, Senators Hawley and Blumenthal are at least publicly in synch on the need to correct the errors in Section 230. “Reform is coming,” Sen. Blumenthal declared in March. All of which is to say that there appears to be both bipartisan and multi-stakeholder consensus building around the idea that platforms can and should be held accountable for promoting harmful material.

Does AI-Enhanced Search Imply Liability?

Notably, one prong of Google’s defense in Gonzalez was that “recommendation” is analogous to search and that delivering search results cannot rise to the level of contributory liability. Whether the Court would agree with this comparison under full examination in a viable case remains an open question. But assuming the Court would not have sided with Google, what might it make of Google’s new Search Generative Experience (SGE)? Still in trial phase for users who choose to enable it, the AI-driven SGE could be the new mode of search, or (if it totally sucks) could tank Google’s core business. As James Vincent writes for The Verge:

… it’s the dynamics of AI — producing cheap content based on others’ work — that is underwriting this change, and if Google goes ahead with its current AI search experience, the effects would be difficult to predict. Potentially, it would damage whole swathes of the web that most of us find useful — from product reviews to recipe blogs, hobbyist homepages, news outlets, and wikis. Sites could protect themselves by locking down entry and charging for access, but this would also be a huge reordering of the web’s economy. In the end, Google might kill the ecosystem that created its value, or change it so irrevocably that its own existence is threatened. 

Hard to predict for sure, and I will not make the attempt. There are, of course, many potential hazards with AI-enhanced search, not the least being more virulent mutations of garbage results (as if misinformation needs any help). But in a Section 230 context, would the deployment of SGE as Google’s new search model increase the likelihood of its liability under the same legal arguments presented in Gonzalez? The “recommendation” algorithm is a form of AI, and if that level of platform influence could be sufficient to find liability, then presumably a more robust use of AI could result in a stronger allegation of liability.

On June 14, Senators Hawley and Blumenthal introduced a two-page bill that would make Section 230 immunity unavailable for service providers “if the conduct underlying the claim or charge involves the use or provision of generative artificial intelligence by the interactive computer service.’’ Presumably, this bill can be seen as performative along with other announcements from Congress that AI has their attention, with various Members promising not to be fooled again into allowing Big Tech to regulate itself. There’s a lot of “We’re on it” messaging coming from the Hill about AI, and we’ll see what comes.

In the meantime, perhaps there is something to the Hawley bill in light of the considerations in Gonzalez and the imminent release of SGE. At first, I sneered at the amendment because generative AI is primarily a tool of production, and Section 230 immunity has little or nothing to do with production. It doesn’t matter whether the harmful material at issue is produced with Midjourney or a box of crayons. But if a generative AI serves as the engine for a new mode of search (i.e., recommendation), then the language in the Hawley/Blumenthal amendment would seem to obviate the need to litigate the question presented in Gonzalez. Congress would be declaring that Google is not automatically shielded from liability.

Considering that we are far from resolving the damage done by the “democratization of information,” it’s tough to feel sanguine about the prospect of AI making search better rather than suck faster. On the other hand, if the adoption of AI in certain core functions of online platforms is a basis for Congress resetting the terms of liability, then perhaps service providers will discover a renewed interest in the original intent of Section 230—an incentive to remove harmful material, not to keep it online and monetize it.

Photo source by: sinenkiy

Let’s Stop Analogizing Human Creators to Machines

Just as it is folly to anthropomorphize computers and robots, it is also unhelpful to discuss the implications of generative AI in copyright law by analogizing machines to authors.[1] In 2019, I explored the idea that “machine learning” could be analogous to human reading if the human happens to have an eidetic memory. But this was a thought exercise, and in that post, I also imagined machine training that serves a computer science or research purpose—not necessarily generative AIs trained on protected works designed to produce works without authors.

In the present discussion, however, certain parties weighing in on AI and copyright seem to advocate policy that is premised on the language and principles of existing doctrine as applicable to the technological processes of both the input and output sides of the generative AI equation. Of course, policy discussions usually begin with the existing framework, but in this instance, it can be a shaky starting place because generative AI presents some unique challenges—and not just for the practice of copyright law.

We should be wary of analogizing machine functions to human activity for the simple reason that copyright law (indeed all law) has never been anything but anthropocentric. Although it is difficult to avoid speaking in terms of machines “learning” or “creating,” it is essential that we either constantly remind ourselves that these are weak, inaccurate metaphors, or that a new glossary is needed to describe what certain AIs may be doing in the world of creative production.

On the input (training) side of the equation, the moment someone says something like, “Humans learn to make art by looking at art, and generative AIs do the same thing,” the speaker should be directed to the break-out session on sci-fi and excused from any serious conversation about applicable copyright law. Likewise, on the output side, comparisons of AI to other technological developments—from the printing press to Photoshop—should be presumed irrelevant unless the AI at issue can plausibly be described as a tool of the author rather than the primary maker of a work of creative expression.

Copyright Office Guidance Highlights Some Key Difficulties

To emphasize the exceptional nature of this discussion, even experts are somewhat confused by both the doctrinal and administrative aspects in the new guidelines published by U.S. Copyright Office directing authors how to disclaim AI-generated material in a registration application. The confusion is hardly surprising because generative AI has prompted the Office to ask an unprecedented question—namely, How was this work made?

As noted in several posts, copyrightability has always been agnostic with regard to the creative process. Copyright rights attach to works that show a modicum of originality, and the Copyright Office does not generally ask what tools, methods, etc. the author used to make a work.[2] But this historic practice was then confronted by the now widely reported applications submitted by Stephen Thaler and Kris Kashtanova, both claiming copyright in visual works made with generative AI.

In both cases, the Copyright Office rejected registration applications for the visual works based on the longstanding, bright-line doctrine that copyright rights can only attach to works made by human beings. In Thaler’s case, the consideration is straightforward because the claimant affirmed that the image was produced entirely by a machine. Kashtanova, on the other hand, asserts more than di minimis authorship (i.e., using AI as a tool) to produce the visual works elements in a comic book.

Whether in response to Kashtanova—or certainly anticipating applications yet to come—the muddiness of the Office guidelines is an attempt to address the difficult question as to whether copyright attaches to a work that combines authorship and AI generation, and how to draw distinctions between the two. This is not only new territory for the Office as a doctrinal matter but is a potential mess as an administrative one.

The Copyright Office has never been tasked with separating the protectable expression attributable to a human from the unprotectable expression attributable to a machine. Even if it could be said that photography has always provoked this tension (a discussion on its own), the analysis has never been an issue for the Office when registering works, but only for the courts in resolving claims of infringement. In fact, Warhol v. Goldsmith, although before SCOTUS as fair use case, is a prime example of how tricky it can be to separate the factual elements of a photograph from the expressive elements.

But now the Copyright Office is potentially tasked with a copyrightability question that, in practice, would ask both the author and the examiner to engage in a version of the idea/expression dichotomy analysis—first separating the machine generated material from the author’s material and then considering whether the author has a valid claim in the protectable expression.

This is not so easy to accomplish in a work that combines author and machine-made elements in a manner that may be subtly intertwined; it begs new questions about what the AI “contributed” to a given work; and the inquiry is further complicated by the variety of AI tools in the market or in development. Then, because neither the author/claimant nor the Office examiner is likely a copyright attorney (let alone a court), the inquiry is fraught with difficulty as an administrative process—and that’s if the author makes a good-faith effort to disclaim the AI-generated material in the first place.

Many independent authors are confused enough by the Limit of Claim in a registration application or the concept of “published” versus “unpublished.” Asking these same creators to delve into the metaphysics implied by the AI/Author distinction seems like a dubious enterprise, and one that is not likely to foster more faith in the copyright system than the average indie creator has right now.

Copyrightability Could Remain Blind But …

It is understandable that some creators (e.g., filmmakers using certain plug-ins) may be concerned that the Copyright Office has already taken too broad a view—connoting a per se rule that denies copyrightability for any work generated with any AI technology. This concern is a reminder that AI should not be discussed as a monolithic topic because not all AI enhanced products do the same thing. And again, this may imply a need for some new terms rather than the words we use to describe human activities.

In this light, one could follow a different line of reasoning and argue that the agnosticism of copyrightability vis-à-vis process has always implied a presumption of human authorship where other factors—from technological enhancements to dumb luck—invisibly contribute to the protectable expression. Relatedly, a photographer can add a filter or plug-in that changes the expressive qualities of her image, but doing so is considered part of the selection and arrangement aspect of her authorship and does not dilute the copyrightability of the image.

Some extraordinary visual work has already been produced by professional artists using AI to yield results that are too strikingly well-crafted to believe that the author has not exerted considerable influence over the final image. In this regard, then, perhaps the copyrightability question at the registration stage, no matter how sophisticated the “filter” becomes, should remain blind to process. The Copyright Office could continue to register works submitted by valid claimants without asking the novel How question.

But the more that works may be generated with little or no human spark, the more this agnostic, status-quo approach could unravel the foundation of copyright rights altogether. And it would not be the first time that major tech companies have sought to do exactly that. It is no surprise that an AI developer or a producer using AI would seek the financial benefits of copyright protection; but without a defensible presence of human expression in the work, the exclusive rights of copyright cannot vest in a person with the standing to defend those rights. Nowhere in U.S. law do non-humans have rights of any kind, and this foundational principle reminds us that although machine activity can be compared to human activity as an allegorical construct, this is too whimsical for a serious policy discussion.

Again, I highlight this tangle of administrative and doctrinal factors to emphasize the point that generative AI does not merely present new variations on old questions (e.g., photography), but raises novel questions that cannot easily be answered by analogies to the past. If the challenges presented by generative AI are to be resolved sensibly, and in a way that will serve independent creators, policymakers and thought leaders on copyright law should be skeptical of arguments that too earnestly attempt to transpose centuries of doctrine for human activity into principles applied to machine activity.

[1] I do not distinguish “human” authors, because there is no other kind.

[2] I say “generally” only because I cannot account for every conversation among claimants and examiners.

Image by boom15th931

Podcast – Tech Designer Carla Diana

This year’s World IP Day theme celebrates Women and IP: Accelerating Innovation and Creativity, and for that reason as well as the fact that artificial intelligence dominates all topics these days, my guest for this episode is the highly innovative Carla Diana, whom I first interviewed in 2014.

Carla is a tech designer, author, and educator. She runs the 4D design program at the Cranbrook Academy of Art in Michigan; she is the lead designer at Diligent Robotics in Austin, Texas; and she is the author of dozens of articles and essays about technology and design. Her most recent book, published in 2021 by Harvard Business Review Press, is My Robot Gets Me: How Social Design Can Make New Products More Human. And we’ll talk about what that means, plus generative AI, driverless cars, ethics in technology, and at least one product I had not imagined was a thing.

Show Contents

  • 00:01:24 – Carla’s background.
  • 00:05:57 – Why good design is social.
  • 00:11:55 – Design modalities & thinking about consumers with disabilities.
  • 00:20:27 – That tech should not mimic human behavior.
  • 00:28:57 – On avoiding innovation for its own sake.
  • 00:36:07 – On ethics in technology.
  • 00:45:51 – Generative AI and the arts.
  • 01:00:55 – Tech solutions for tech problems (e.g. Glaze for visual artists).
  • 01:05:32 – Self-driving vehicles.
  • 01:09:30 – Economic & social implications of a driverless world.
  • 01:15:26 – Combining design and ethics.