IP x Social Justice Quintet

 Five articles that use intellectual property law creatively to promote social justice for cutting-edge technologies, all available for free.

Dystopian Trademark Revelations (forthcoming 2023)

Pairing information in trademark registrations with their underlying namesakes helps the public understand technologies’ dystopian implementations. 55 Conn. L. Rev. 1 (forthcoming 2023)

Uncovering dystopian technologies is challenging. Non-disclosure agreements, procurement policies, trade secrets, and strategic obfuscation collude to shield the development and deployment of these technologies from public scrutiny until it’s too late to combat them with law or policy. But occasionally, exposing dystopian technologies is simple. Corporations choose technology trademarks inspired by dystopian philosophies, novels or real life, all warnings that their aspirations are dystopian as well. That pronouncement is not necessarily trumpeted on Twitter or corporate websites, however. It’s revealed in a more surprising place: trademark registrations at the U.S. Patent and Trademark Office (PTO).

In exchange for registrations, the PTO demands disclosure of details about applied-for trademarks. Those include the mark itself as well as information about how the mark will be used, forcing corporations to admit their intent for their technologies. But those details do not always provide the full picture. PTO disclosures can be strategically supplemented with knowledge of the dystopian inspiration for the marks to understand corporations’ plans for their products. This Article uses the marks PALANTIR for big data analytics, PANOPTO for classroom recording systems, and MECHANICAL TURK for on-demand work to illustrate the power of coupling trademark registrations with underlying namesakes to understand technologies’ dystopian implementations. Dystopian trademarks signal dystopian technologies, and the public is well-positioned to seek them out and develop strategies to combat their entrenchment.

Trademarks as Surveillance Transparency (2021)

Federal trademark applications are a free, public, and easily accessible source of information about secret surveillance technology. 36 Berkeley Tech. L. J. (2021)

We know very little about the technologies that watch us. From cell site simulators to predictive policing algorithms, the lack of transparency around surveillance technologies makes it difficult for the public to engage in meaningful oversight. Legal scholars have critiqued various corporate and law enforcement justifications for surveillance opacity, including contract and intellectual property law. But the public needs a free, public, and easily accessible source of information about corporate technologies that might be used to watch us. To date, the literature has overlooked a free, extensive, and easily accessible source of information about surveillance technologies hidden in plain sight: federal trademark filings.

This Essay examines the powerful and unexplored role of trademark law in exercising oversight within and beyond surveillance. Trademark law promotes access to information, and the federal trademark application process—long overlooked by scholars—demands extensive public disclosures that reveal a wealth of information about surveillance technologies. This Essay leverages examples from real trademark applications to explore how journalists, researchers, and civil society can use the detailed disclosures in trademark applications for transparency. I conclude that trademark law can be a powerful tool for correcting longstanding information asymmetries between the watchers and the watched by empowering the public to watch back.

TLDR:

Reviewed by Jessica Silbey, Fixing Information Asymmetry Through Trademark Search, Jotwell
Amanda Levendowski, “How Can We Learn About the AI Systems that Might Be Used to Surveil Us? The Federal Trademark Register Has Answers,” Guest Blogger for Ethics and Governance of AI Initiative (Oct. 11, 2018)

Resisting Face Surveillance with Copyright Law (2022)

Invasive face surveillance is fueled by copyrightable profile pictures—and it isn’t likely to be fair use.
100 N. C. L. Rev. 1015 (2022)

Face surveillance is animated by deep-rooted demographic and deployment biases that endanger marginalized communities and threaten the privacy of all. But current approaches have not prevented its adoption by law enforcement. Some companies have offered voluntary moratoria on selling the technology, leaving many others to fill in the gaps. Legislators have enacted regulatory oversight at the state and city levels, but a federal ban remains elusive. Both approaches require vast shifts in practical and political will, each with drawbacks. While we wait, face surveillance persists. This Article suggests a new possibility: face surveillance is fueled by unauthorized copies and reproductions of photographs, and resisting face surveillance compels us to consider countering it with copyright law.

So why haven’t face surveillance companies been overwhelmed with copyright infringement litigation? Fair use. This Article lays out the litigation landscape before analyzing the recent Supreme Court decision in Google v. Oracle, alongside other key fair use cases, to examine why this complex doctrine may permit many uses of machine learning without allowing face surveillance to copy and reproduce online profile pictures. Some face surveillance companies claim to be transformative search engines, but their business models are more like private subscription services that are rarely found to be fair use. And scraping profile pictures harms the unique licensing market for these photographs, which grows as companies and researchers increasingly reject scraped photos as sources of face analysis training data. This Article concludes that copyright law could curtail face surveillance without waiting for companies or Congress to catch up.

TLDR:

Reviewed by Ifeoma Ajunwa, Confronting Surveillance, Jotwell

How Copyright Law Can Fix
Artificial Intelligence's Implicit Bias Problem (2018)

Using copyrighted works as training data for many AI systems is not only a fair use, but one that can quite literally promote fairness. 93 Wash. L. Rev. 579 (2018)

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright.

Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law—the fair use doctrine—has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems.

TLDR:
Amanda Levendowski, ”Can Fair Use Make for Fairer AI?”, Public Books (Oct. 22, 2019)
Reviewed by Michael W. Carroll, Using Fair Use to Reduce Algorithmic Bias, Jotwell

Media Coverage:

April Glaser, “Who Trained Your AI,” Slate (Oct. 24, 2017)
Louise Matsakis, “Copyright Law Makes Artificial Intelligence Bias Worse,” Vice Motherboard (Oct. 31, 2017)
Janus Kopfstein, “Bad Copyright Laws Are Creating Junky, Biased AI,” Vocativ (Apr. 5, 2017)

Using Copyright to Combat Revenge Porn (2014)

Copyright law can be a powerful tool for removing images of nonconsensual pornography from the Internet.
3 N.Y.U. J. Intell. Prop. & Ent. L. 422 (2014)

The phenomenon of “revenge porn” – defined as sexually explicit images that are publicly shared online, without the consent of the pictured individual – has attracted national attention. Victims of revenge porn often suffer devastating consequences, including losing their jobs, but have had limited success using tort laws to prevent the spread of their images.

Victims need a remedy that provides takedown procedures, civil liability for uploaders and websites, and the threat of money damages. Copyright law provides all of these remedies. Because an estimated 80 percent of revenge porn images are “selfies,” meaning that the subject and the photographer are one in the same, the vast majority of victims can use copyright law to protect themselves.

Although copyright is not a perfect solution, it provides a powerful tool to combat revenge porn. In Part I, I examine how Section 230 protects revenge porn traffickers, like IsAnyoneUp, from liability. Part II discusses why harassment, stalking and privacy laws are often inadequate means of fighting revenge porn. In Part III, I explain why existing and proposed legislation presents problems for both victims and free speech. Finally, Part IV outlines why copyright functions as a powerful tool to combat revenge porn.

TLDR:
Amanda Levendowski, “Our Best Weapon Against Revenge Porn: Copyright Law?,” The Atlantic (Feb. 4, 2014)

Media Coverage:
Caitlin Dewey, “How Copyright Law Became the Best Defense Against Revenge Porn,” Washington Post (Sept. 8, 2014)
PJ Vogt, “Could Copyright Law Be the Best Solution to Revenge Porn?,” On the Media (Dec. 12, 2013)