Hugh Stephens: AI issues should be resolved through legislation, not litigation

Photo by imaginima

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There is an ongoing struggle between the tech world of artificial intelligence training and the cultural world of content creation. It has led to lots of litigation but also an increasing number of licensing agreements, the obvious market solution.

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Anyone who has seen the striking graphic “Who is Suing Whom in AI,” created by the design website Information is Beautiful, will be struck by the enormity and breadth of the issue, which is so cleverly displayed, with the big AI developers such as Perplexity, Anthropic, Meta, Google, OpenAI, Midjourney, Cohere and others at the centre, and the creators (every content entity from Condé Nast, Getty Images, Universal Music Group, CNN, Disney and Thomson Reuters to Elsevier, Dow Jones, the New York Times and others) ranged around the periphery, a stunning visual encompassing more than 100 lawsuits in the United States. That graphic was up-to-date as of June.

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Since then, at least one more major lawsuit has been filed, by a group of textbook authors against Meta. The graphic does not include the first such case in Canada, where a group of media organizations (The Canadian Press, Torstar, the Globe and Mail, Postmedia and CBC/Radio-Canada) is suing OpenAI, or the Getty Images case in the U.K., or indeed any cases outside the U.S. From this graphic, it would seem that to resolve the issue of how copyrighted content is going to be used in AI development and training, litigation is the inevitable route.

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But as A.G. Sulzberger, chairman and publisher of the New York Times, noted in a brilliant speech to WAN-IFRA’s World News Media Congress, “Lawsuits are slow and expensive — ours has already stretched two-and-a-half years and cost over $20 million. As AI companies are doubtless aware, most news organizations lack the resources to go to court to enforce their rights.”

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In a perfect world, AI developers would obtain their inputs through the market on the basis of permission, which would encompass both compensation (in most cases) plus transparency or accountability — i.e., documenting what content was used. But we don’t live in a perfect world, which is why we have the rule of law and courts to enforce those laws. In some cases, AI platforms did begin negotiations with rights-holders, but when it was not possible to reach an agreement, the AI industry switched tactics and took the content anyway, arguing it was legal to do so for a variety of reasons.

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This is precisely the scenario that led to the New York Times suing OpenAI. These cases are even more egregious because there was initially a tacit acknowledgement by the user that the content had value. Then, when the price or conditions did not suit the potential licensee, suddenly it was OK to take the content anyway under the guise of fair use. Various arguments have been deployed, ranging from the claim that no copying actually occurs, to the dubious assertion that what is copied is data not content, to the invocation of the U.S. “transformation” doctrine.

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A study by The Atlantic (AI’s Memorization Crisis: Large Language Models Don’t “Learn” — They Copy. And That Could Change Everything For The Tech Industry) convincingly demonstrated the uncomfortable truth that LLMs can reproduce long excerpts from books they have been trained on. The inputs are not just ones and zeros, they are content — someone else’s content that was taken without permission.

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As Rod Sims, former chair of the Australian Competition and Consumer Commission, put it in a recent opinion piece in National Post, “What other sector refuses to negotiate with suppliers and instead goes to government to bypass such a step?”