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Compendium of U.S. Copyright Office Practices (3d ed. 2021) | Chapter 300 Copyrightable Authorship: What Can Be Registered

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What determines the copyrightability of a work?

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What determines the copyrightability of a work?

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According to Compendium (Third) § 305, 306, 308, a copyrightable work must be:

Original

Fixed in a tangible medium

Created by a human being

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Understanding CC Licenses and Generative AI - Creative Commons

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Key Takeaways:

1. Creators using generative AI can apply Creative Commons (CC) licenses to their contributions, even though AI-generated portions may be uncopyrightable.

2. The use of CC0 is encouraged for works lacking significant human creativity to clarify their intellectual property status and support public domain growth.

"Understanding CC Licenses and Generative AI" by Kat Walsh is licensed under CC BY 4.0

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Understanding CC Licenses and Generative AI - Creative Commons

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Many wonder what role CC licenses, and CC as an organization, can and should play in the future of generative AI. The legal and ethical uncertainty over using copyrighted inputs for training, the uncertainty over the legal status and best practices around works produced by generative AI, and the implications for this technology on the…

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AI and Copyright: An Ongoing Initiative and Dialogue

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AI and Copyright: An Ongoing Initiative and Dialogue

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The legal framework around AI and copyright is likely to evolve. The U.S. Copyright Office has a dedicated AI Initiative webpage. Stay informed on the latest updates!

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Copyright and Artificial Intelligence | U.S. Copyright Office

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The U.S. Copyright Office’s AI Initiative in 2024

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The U.S. Copyright Office’s AI Initiative in 2024

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On March 26, the U.S. Copyright Office released a blog post outlining their next steps for 2024:

1. Publishing a multi-section report on AI's impact on copyright (digital replicas in spring, AI-generated works in summer, and training AI models on copyrighted works later).

2. Updating the Compendium of U.S. Copyright Office Practices with guidelines on AI-generated material.

3. Collaborating with economists to research copyright and AI intersections.

4. Providing ongoing updates on the AI initiative via their webpage.

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Looking Forward: The U.S. Copyright Office’s AI Initiative in 2024 | Copyright

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This blog post previews the next steps of the U.S. Copyright Office’s comprehensive initiative to examine the copyright implications of the current forms of generative AI and shares highlights from a recent copyright and AI initiative update letter to Congress from Register of Copyrights Shira Perlmutter.

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"Defining the “Open” in Open Content and Open Educational Resources" by David Wiley is licensed under CC BY 4.0

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The terms "open content" and "open educational resources" describe any copyrightable work (traditionally excluding software, which is described by other terms like "open source") that is either (1) in the public domain or (2) licensed in a manner that provides everyone with free and perpetual permission to engage in the 5R activities:

1. Retain - make, own, and control a copy of the resource (e.g., download and keep your own copy)

2. Revise - edit, adapt, and modify your copy of the resource (e.g., translate into another language)

3. Remix - combine your original or revised copy of the resource with other existing material to create something new (e.g., make a mashup)

4. Reuse - use your original, revised, or remixed copy of the resource publicly (e.g., on a website, in a presentation, in a class)

5. Redistribute - share copies of your original, revised, or remixed copy of the resource with others (e.g., post a copy online or give one to a friend)

Legal Requirements and Restrictions Make Open Content and OER Less Open

While a free and perpetual grant of the 5R permissions by means of an "open license" qualifies a creative work to be described as open content or an open educational resource, many open licenses place requirements (e.g., mandating that derivative works adopt a certain license) and restrictions (e.g., prohibiting "commercial" use) on users as a condition of the grant of the 5R permissions. The inclusion of requirements and restrictions in open licenses make open content and OER less open than they would be without these requirements and restrictions.

There is disagreement in the community about which requirements and restrictions should never, sometimes, or always be included in open licenses. For example, Creative Commons, the most important provider of open licenses for content, offers licenses that prohibit commercial use. While some in the community believe there are important use cases where the noncommercial restriction is desirable, many in the community strongly criticize and eschew the noncommercial restriction.

As another example, Wikipedia, one of the most important collections of open content, requires all derivative works to adopt a specific license - CC BY SA. MIT OpenCourseWare, another of the most important collections of open content, requires all derivative works to adopt a specific license - CC BY NC SA. While each site clearly believes that the ShareAlike requirement promotes its particular use case, the requirement makes the sites' content incompatible in an esoteric way that intelligent, well-meaning people can easily miss.

Generally speaking, while the choice by open content publishers to use licenses that include requirements and restrictions can optimize their ability to accomplish their own local goals, the choice typically harms the global goals of the broader open content community.

Poor Technical Choices Make Open Content Less Open

While open licenses provide users with legal permission to engage in the 5R activities, many open content publishers make technical choices that interfere with a user's ability to engage in those same activities. The ALMS Framework provides a way of thinking about those technical choices and understanding the degree to which they enable or impede a user's ability to engage in the 5R activities permitted by open licenses. Specifically, the ALMS Framework encourages us to ask questions in four categories:

1. Access to Editing Tools: Is the open content published in a format that can only be revised or remixed using tools that are extremely expensive (e.g., 3DS MAX)? Is the open content published in an exotic format that can only be revised or remixed using tools that run on an obscure or discontinued platform (e.g., OS/2)? Is the open content published in a format that can be revised or remixed using tools that are freely available and run on all major platforms (e.g., OpenOffice)?

2. Level of Expertise Required: Is the open content published in a format that requires a significant amount technical expertise to revise or remix (e.g., Blender)? Is the open content published in a format that requires a minimum level of technical expertise to revise or remix (e.g., Word)?

3. Meaningfully Editable: Is the open content published in a manner that makes its content essentially impossible to revise or remix (e.g., a scanned image of a handwritten document)? Is the open content published in a manner making its content easy to revise or remix (e.g., a text file)?

4. Self-Sourced: It the format preferred for consuming the open content the same format preferred for revising or remixing the open content (e.g., HTML)? Is the format preferred for consuming the open content different from the format preferred for revising or remixing the open content (e.g. Flash FLA vs SWF)?

Using the ALMS Framework as a guide, open content publishers can make technical choices that enable the greatest number of people possible to engage in the 5R activities. This is not an argument for "dumbing down" all open content to plain text. Rather it is an invitation to open content publishers to be thoughtful in the technical choices they make - whether they are publishing text, images, audio, video, simulations, or other media.

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Defining the “Open” in Open Content and Open Educational Resources – improving learning

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The terms “open content” and “open educational resources” describe any copyrightable work (traditionally excluding software, which is described by other terms like “open source”) that is either (1) in the public domain or (2) licensed in a manner that provides everyone with free and perpetual permission to engage in the 5R activities:

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UTA Libraries Blog: Using Generative AI in OER Creation—What We Know Now

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UTA Libraries Blog: Using Generative AI in OER Creation—What We Know Now

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"Using Generative AI in OER Creation—What We Know Now" by Jessica McClean is licensed under CC BY-NC 4.0

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It has been impossible to avoid the topic of artificial intelligence (AI) in higher education so far in 2024, and we are only at the beginning of what promises to be a long and complex conversation touching on ethics, student success, privacy, copyright, and much more. While it is exciting to explore the early stages of a new technology that will have a lasting impact on the way we teach and learn, it can be challenging to keep track of what we know for sure and what is still uncertain. On the broader topic of AI, the Artificial Intelligence at UTA page contains news, events, and local guidance on using AI in instruction and research. This blog post will give a summary of some of the current rules, guidelines, and best practices for using AI in open educational resource (OER) creation, as well as some thoughts on how this topic might evolve in the future.

Copyright

Ethical reuse of copyrighted material is at the heart of OER work. OER are defined by their open licenses, which outline permissions granted by the copyright holder to the OER user. Copyright is automatically granted to a creator when an original work of authorship is fixed in a tangible form and lasts (in the United States) for the lifetime of the creator plus 75 years, after which the work enters the public domain. Open licenses do not remove or replace copyright but rather allow the copyright holder to reserve some of their authorial rights while providing free (i.e., no cost) access and additional usage permissions to others. While permissions vary depending on the license selected, every Creative Commons license contains the BY element, requiring users to attribute the original work to its author whenever they use, share, or modify an open resource.

With all of that in mind, copyright for AI-generated material is one of the most important points we must consider when using AI in OER creation. Unfortunately, there is still a lot of uncertainty and disagreement about what is or is not covered by copyright. Legal cases to determine copyrightability of AI-generated content thus far have focused heavily on the term "authorship." According to legal precedent as of the writing of this blog post, authorship applies only to human authors-remember the famous case of the monkey selfie? While a human is involved in the initiation stage of the AI process through writing and refining prompts, courts have ruled that the AI-generated product is not human-authored and therefore not copyrightable. As a result, any work generated by AI would automatically be in the public domain and not subject to copyright laws.

If AI-generated resources, such as images and graphics, are in the public domain, that's great news for OER creation, right? Not so fast-there are still a lot of tangled copyright issues being worked through in legal cases that may tighten up guidelines in the future. While we currently can't claim copyright on the output of the AI process, that is not the case for most of the content used to train the AI models. Lawsuits against tech companies for misusing copyrighted material in training AI are pending in several industries, and Rep. Adam Schiff (CA) has proposed a bill that would require companies to be more transparent about using copyrighted material in this way. In the future, content produced by an AI model trained on copyrighted material might be considered a derivative of an original work or a work of joint authorship.

During this uncertain period, OER authors planning to use AI-generated content can follow some record-keeping and documentation practices that may prove useful if legislation or legal guidelines change in the future. Maintaining detailed documentation is a best practice for reusing openly licensed material, so we can apply this same procedure to record-keeping for AI-generated content. We recommend keeping track of what content is being reused, where in the OER it is being used, and what license it has been given. A useful example document has been shared as part of the Open Education Network's Pub101 curriculum. For AI content, it would also be helpful to note which AI tool was used to create the content and the submitted prompt.

Future Opportunities for Using AI in OER

While the copyright issues seem daunting, OER provides some unique opportunities for using AI. At the UT System conference on Innovating Teaching and Learning in the Era of Generative AI earlier this year, Posie Aagaard of UTSA and Dr. Art Brownlow of UTRGV proposed using AI to help generate ancillary materials, such as question banks for open-source homework platforms. While dedicated faculty, including those here at UTA, have contributed to the increasing number of comprehensive and high-quality OER available, creation of quality ancillary materials has been slower. Setting up open-source homework platforms, such as WeBWorK, requires an investment of time and effort to generate enough content to make the platform useful. This proposed AI-assisted approach should be able to generate a large amount of ancillary material quickly and potentially skirts the copyright issues discussed above because the AI would be trained on the corresponding OER textbook, which would, of course, be openly licensed for reuse.

Even without considering AI assistance, open-source homework platforms may prove relevant over the next year as an even more affordable alternative to inclusive-access models, which are being closely examined at the federal level. Proposals from the Department of Education include making inclusive access programs opt-in rather than opt-out, as they currently are.

Stay informed on AI

I look forward to seeing how this topic develops over the next academic year and discussing future opportunities and challenges. To stay up to date on developments in this area, keep an eye on the Artificial Intelligence at UTA page. We have also added a new OER and AI channel to our Open Educational Resources Team to collect relevant internal and external workshops and webinars, best practices, and other helpful resources. The OER Teams channel is open to all UTA faculty, staff, and students. If you are interested in talking more about using AI in OER or about OER in general, please email oer@uta.edu.

The cover image for this blog post is "Monkey Selfie with Banner" by Jessica McClean, licensed . The original image is in the public domain and was modified to add the orange "Open @ UTA Libraries" banner.

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Using Generative AI in OER Creation-What We Know Now

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It has been impossible to avoid the topic of artificial intelligence (AI) in higher education so far in 2024, and we are only at the beginning of what promises to be a long and complex conversation touching on ethics, student success, privacy, copyright, and much more.

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The Lawsuits That Could Shape the Future of AI and Copyright Law

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The Lawsuits That Could Shape the Future of AI and Copyright Law

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Refer to the segment from 0:26 to 3:26, where the lawyer discusses three pivotal lawsuits influencing the future of AI and copyright law:

1. Thaler v. Perlmutter (2023)

2. Naruto Monkey Selfie case (2015)

3. Zarya of the Dawn (2023)

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“Copyright Lawyer Explains Drake AI Song and More | WSJ” YouTube, uploaded by The Wall Street Journal, 15 April 2024, https://youtu.be/Cds-p_QG1-g?si=xA-Cq-e4c1G9NBU_&t=26

Standard YouTube License

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Copyright Lawyer Explains Drake AI Song and More | WSJ

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Congressional Research Service Report (May 11, 2023)

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CRS Report: Generative Artificial Intelligence and Copyright Law

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CRS Report: Generative Artificial Intelligence and Copyright Law

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Other thoughts? Feel free to add your opinions in the comments.

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Should AI-generated work be copyrightable?

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Yes, AI-generated work should be fully copyrightable.

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Yes, but only if there is significant human contribution involved.

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No, AI-generated work should not be copyrightable at all.

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I'm not sure, more regulation and clarity are needed.

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Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence (Issued March 16, 2023)

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Summary:

Outputs from AI, on their own cannot obtain copyright protection.

However, if a human selects, arranges, or modifies AI output in a sufficiently creative way, the resulting work might be eligible for copyright.

In such cases, the copyright will only protect the human-authored aspects of the work, not the AI-generated portions.

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TCC Libraries Research Guide: Creating OER with AI

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TCC Libraries Research Guide: Creating OER with AI

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"Creating OER with AI" by TCC Libraries is licensed under CC BY 4.0

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Research Guides: ChatGPT and AI Tools Faculty Research Guide: Creating OER with AI

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Research Guides: ChatGPT and AI Tools Faculty Research Guide: Creating OER with AI