This Note provides a general review of the current state of Facial Recognition Technology (FRT), including Illinois state regulation and past federal regulation attempts. This Note asserts that even as datasets become more diverse and “fairer,” FRT may still have discriminatory impacts on minority populations, as evidenced by a few highlighted examples that focus on race and gender. This Note suggests that one option to mitigate these dangers is to implement national regulations for its use. Ultimately, this Note suggests what a federal regulation might look like, informed by the societal impacts discussion.

After an introduction in Section I, Section II provides a brief overview of the history of FRT, how it works, and where it is used. Next, Section III dives into the Illinois Biometric Information Privacy Act (BIPA), focusing on what distinguishes it from the plaintiff’s perspective and recent amendments to the Act. To expand on this discussion, Section IV discusses two case illustrations that exemplify what BIPA litigation should look like and the impact that these court holdings have had on the statute. Section V looks at the past and current federal FRT regulation, including past failed federal enactment attempts. Finally, Section VI discusses the societal impacts of FRT and the benefits of a united federal regulatory system. This Note concludes with an idealized federal regulation suggestion, grounded in BIPA and the American Data Privacy and Protection Act.

The focus of this Note is to provide a foundational understanding of FRT and ultimately provide an explanation for how the information fed into FRT models is analyzed. By looking through the lens of BIPA and homing in on the inner workings of a single state’s statute, this Note focuses on a few of the current rights and regulations in place and how they are used to protect against the misappropriation of biometric data. As the Note shifts toward a discussion about the societal impacts of FRT, the focus is on the discriminatory outcomes as a main danger of FRT.

The introductory information laid before the societal implications discussion helps to highlight how the impacts discussed are a direct result of FRT systems and current regulations. This Note contributes to the current discussion about potential bias in face surveillance and biometrics technology, seeking to fill gaps in the current literature by using BIPA as a lens for viewing current regulations, combined with a societal impact analysis. This Note presents an idealistic suggestion for federal regulation that would harmonize national usage and protections for FRT and biometrics in the future.

Generative AI can rapidly output vast amounts of expressive content, some of which has great value for society. This new computational process also raises a deep question of fairness: Will the original creators of the content used to train these systems share in the value they create? The question becomes particularly urgent as the potential effects of generative AI on markets for creative works become clearer. Artists with a distinctive style may find it nearly impossible to sell their new work when very low-cost substitutes can be generated automatically. News publishers whose content can now be paraphrased by generative AI systems without violating copyright laws may lose significant advertising revenue from readers who no longer need to click through to publishers’ websites. Millions of workers may be wholly or partially displaced by generative AI trained on their works.

Numerous scholars have begun to address this issue. Some have focused on challenging generative AI providers’ claims that their ingestion of copyrighted works for training models and outputting new works is fair use. Others have conceded or bracketed the fair use question and proposed levies or compulsory licenses to compensate for these uses. We take a distinct approach, proposing a new right for copyright holders with respect to AI training using their work. This protection is appropriate given massive AI systems’ ability to process vast amounts of information far faster and less expensively than humans can. An exclusive right to license AI training, called a “learnright” for short, would enable copyright holders to claim some share in the revenues arising out of automated systems that learn from covered material.

This essay examines the rationale and potential mechanisms for implementing such laws. It explains the high degree of legal uncertainty surrounding the many current lawsuits against generative AI providers, and it proposes learnrights to complement the existing exclusive rights guaranteed to copyright holders. Given the many sources from which AI can “learn,” market mechanisms would likely permit a fair and reasonable degree of revenue sharing pursuant to copyright holders’ assertion of their learnrights. Compensation for learnrights would also redress some striking imbalances apparent in current copyright policy that favor mechanical processing of texts over human engagement with them.

A significant number of blockchain users is U.S.-based. However, regulatory ambiguity imposes barriers for U.S. users to fully and legally embrace blockchain technology, especially when it comes to cryptocurrencies. The industry remains rife with bad actors and inadequate regulation that consequently harm investors. Recently, a phenomenon known as cryptocurrency airdrops—widely viewed as “free” token distributions to incentivize blockchain adoption and promote decentralization—has drawn significant attention. But is this just another sham under the guise of the commonly touted “decentralization” ethos of blockchain technology?

The Securities and Exchange Commission (SEC) has maintained that most cryptocurrencies are securities subject to regulation. However, it has not clarified whether airdrops are securities, including the manner in which they are issued. In response, members of Congress have urged the SEC to provide clarity by emphasizing concerns about stifled innovation, missed financial opportunities, and the potential offshoring of blockchain advancements. Similarly, blockchain attorneys contend that because airdrops are freely distributed, they are not securities and are therefore exempt from SEC regulation.

This Article reveals, first and foremost, that, contrary to popular belief, airdrops are not free despite their misleading label. Airdrops have evolved from mere giveaways toward a model of financial reciprocity, where projects capitalize on cryptocurrency distributions to incentivize user adoption and investment. This finding reflects an increasing number of startup projects vying for limited user engagement, the process of which creates mutual financial interdependence between projects and their users. This Article argues that as long as airdrops involve such reciprocity, they constitute securities that fall squarely within the purview of SEC regulation.

Furthermore, this Article contends that while the public, members of Congress, and blockchain attorneys’ concerns regarding airdrops are valid, they overlook several critical issues. From a systemic perspective, airdrops fail to advance decentralization as the core ethos of blockchain technology. From a legal standpoint, airdrops create three alarming problems. First, project-user interactions often create express or implied-in-fact contracts, which may expose blockchain projects to civil liabilities over disputes about “fair” airdrop allocations. Second, these contractual relationships, while not necessary, evidence a quid pro quo financial arrangement that supports the economic reality analysis in the Howey test. This renders the issuance of airdrops investment contracts and, therefore, securities. Third, airdrops encourage unethical and manipulative practices, such as deceptive marketing, wash sales, and insider trading, that ultimately harm genuine users and investors. The totality of these problems necessitates regulatory oversight for adequate user protection.