Defending Disclosure in Software Licensing
The authors thank George Hay, Stewart Schwab, and the faculties of Boston University School of Law and Cornell Law School for their comments. Daniel Forester provided excellent research assistance.
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This Article surveys prominent kinds of disclosures in contract law—of facts, contract terms, and performance intentions. We show why the disclosure tool, although subject to substantial criticism, promotes important social values and goals, including efficiency, autonomy, corrective justice, fairness, and the legitimacy of the contract process. Further, proposals to replace disclosure with other alternatives are unrealistic because they are too expensive or complex. Our working example is the American Law Institute’s Principles of the Law of Software Contracts.
Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.
Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.
We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.
Critics of generative AI often describe it as a “plagiarism machine.” They may be right, though not in the sense they mean. With rare exceptions, generative AI doesn’t just copy someone else’s creative expression, producing outputs that infringe copyright. But it does get its ideas from somewhere. And it’s quite bad at identifying the source of those ideas. That means that students (and professors, and lawyers, and journalists) who use AI to produce their work generally aren’t engaged in copyright infringement. But they are often passing someone else’s work off as their own, whether or not they know it. While plagiarism is a problem in academic work generally, AI makes it much worse because authors who use AI may be unknowingly taking the ideas and words of someone else.
Disclosing that the authors used AI isn’t a sufficient solution to the problem because the people whose ideas are being used don’t get credit for those ideas. Whether or not a declaration that “AI came up with my ideas” is plagiarism, failing to make a good-faith effort to find the underlying sources is a bad academic practice.
We argue that AI plagiarism isn’t—and shouldn’t be—illegal. But it is still a problem in many contexts, particularly academic work, where proper credit is an essential part of the ecosystem. We suggest best practices to align academic and other writing with good scholarly norms in the AI environment.
Beware dark patterns. The name should be a warning, perhaps alluding to the dark web, the “Dark Lord” Sauron, or another archetypically villainous and dangerous entity. Rightfully included in this nefarious bunch, dark patterns are software interfaces that manipulate users into doing things they would not normally do. Because of these First Amendment complications, the constitutionality of dark pattern restrictions is an unsettled question. To begin constructing an answer, we must look at how dark patterns are regulated today, how companies have begun to challenge the constitutionality of such regulations, and where dark patterns fall in the grand scheme of free speech. Taken together, these steps inform an approach to regulation going forward.