By: Dustin Lennon-Jones

The introduction of name, image, and likeness deals has resulted in massive amounts of money being paid to college athletes. In its first year, Opendorse, the leading NIL marketplace, valued the total market for deals at around $917 million. This number could reach a staggering $2.5 billion with the introduction of revenue sharing next year. As schools prepare for a new revenue sharing model, the applicability of Title IX raises important questions on how this money will be distributed.

Title IX and College Athletics

Under Title IX, codified as 20 U.S.C. §§ 1681-88, educational programs that receive federal funding are prohibited from discriminating on the basis of sex. In higher education, Title IX applies broadly across numerous areas, including intercollegiate athletics. Department of Education regulations, enforced by the Office of Civil Rights (OCR), require that schools offer equal benefits, opportunities, and treatment to its men’s and women’s sports programs. Equal opportunity does not require a school to offer the same sports to both men and women, or even the same number of sports. Rather, opportunities should be proportional to the school’s enrollment. This means that if a school’s undergraduate enrollment is 55% women, then approximately 55% of its athletic opportunities should go to women. This is also true for financial assistance, so scholarships must be available on a proportional basis to the number of participants in men’s and women’s athletics. Equal benefits and treatment means that men’s and women’s sports teams are treated equally in terms of equipment and supplies, scheduling, facilities, recruitment, and much more. 

The Current NIL Landscape

Though the National Collegiate Athletic Association (NCAA) began allowing student-athletes to profit off of their own name, image, and likeness (NIL) in 2021, schools were forbidden from paying their athletes themselves. Instead, student-athletes signed deals with third parties, often facilitated or funded by NIL “collectives.” These are independent organizations not officially affiliated with a given university and are typically funded through donations. However, this characterization is largely in name only. Collectives are often founded by wealthy fans and alumni of a school, utilized explicitly to recruit athletes to their school. For example, the top ranked high school football prospect in the country, Bryce Underwood, flipped his commitment from Louisiana State University to the University of Michigan after he was reportedly offered an NIL deal of $12 million. The NIL collective “Champions Circle” issued a statement following this, welcoming Underwood and thanking “our Founding Members and others associated with Champions Circle who have worked tirelessly behind the scenes to make it possible to continue our work building championship teams at Michigan.” Nonetheless, NIL collectives are officially independent from schools and do not receive federal funding, making them currently beyond the reach of the requirements of Title IX.

Proposed Settlement in House v. NCAA

On October 7, 2024, a federal judge in California granted preliminary approval to a settlement agreement in House v. NCAA, which would allow schools to share 22% of their annual revenue with student-athletes, capped at $22 million. While this doesn’t seem to fit into the traditional idea of “financial assistance” discussed in Title IX, it is not entirely distinct either. The settlement would also remove scholarship caps, and any money spent on scholarships would count toward the $22 million revenue sharing limit. Which begs the question; since schools will be paying student-athletes directly through revenue sharing, and this money is, whether directly or indirectly, funding scholarships, should it be subject to the requirements of Title IX? Or, since athletic revenue is not brought in equally across the various teams, should it be distributed proportionally to the individual teams?

The Biden Administration OCR Fact Sheet

On January 16, the Biden Administration provided some guidance on this financial assistance question. The OCR issued a fact sheet announcing that NIL payments are “financial assistance” within the meaning of Title IX and must therefore be made proportionately to male and female athletes. It is worth noting that a fact sheet is not legally binding, but instead are signals to members of an industry on how a department intends to interpret the law in the future. When leadership changes, these interpretations are easily changed as well. And, sure enough, the Trump Administration reversed course on February 11 and rescinded the fact sheet. In a statement, acting assistant secretary for civil rights Chris Trainor said that Title IX “says nothing about how revenue-generating athletics programs should allocate compensation among student athletes” and would require clear legal authority to support it, which “does not exist.” 

What Happens Next?

Regardless of who you agree with, Trainor is right about one thing: there is no clear legal authority. Title IX and the resulting regulations were written when the debate was whether or not providing cream cheese with bagels was an improper benefit. Now, we are asking questions with million-dollar answers. How it shakes out will have immense consequences for schools and their student-athletes. For example, Texas Tech University announced their plans to share 74% of the revenue with the football team, with men’s basketball receiving around 18%. Women’s basketball, baseball, and women’s volleyball would combine to share the remaining 8%. While not exact, this is approximately tracking the revenue attributable to each of those teams. If revenue sharing payments were subject to Title IX, such a distribution would be unlawful, as Texas Tech’s undergraduate enrollment is approximately 50% women. Regardless of how schools choose to make these payments, distributions like Texas Tech’s seem primed for a Title IX lawsuit, especially while Congress remains on the sidelines.

#NIL #TitleIX

By: Esha Kher

Precision agriculture is revolutionizing modern farming by integrating advanced technologies, particularly artificial intelligence. AI has improved farming efficiency by utilizing data-driven decision-making, autonomous machinery, and predictive analytics to optimize resource use and enhance climate resilience. This transformation is driven by three key technologies: machine learning, which provides actionable insights; robotics, which automates tasks like harvesting; and Internet of Things devices, such as sensors and drones that enable real-time crop monitoring. These advancements allow farmers to make informed choices about irrigation, fertilization, and pest control, thus boosting efficiency, reducing resource waste, and enhancing climate resilience.

As AI-driven agriculture expands—with the market projected to grow from $1.7 billion in 2023 to $4.7 billion by 2028—the vast collection and processing of agricultural data raises significant concerns about data privacy. Addressing these legal challenges is essential to ensuring that precision agriculture continues to advance while also maintaining responsible data governance and ethical practices.

Data privacy risks in precision agriculture

Data ownership in precision agriculture presents a complex legal challenge, as multiple stakeholders, including agricultural technology providers (ATPs), farmer cooperatives, policymakers, and private-sector entities, are involved. The absence of a universal legal framework defining data ownership in agriculture creates ambiguities around rights related to access, modification, and distribution. 

Many precision farming solutions rely on cloud-based platforms, data analytics firms, and equipment manufacturers that collect and process agricultural data. Studies indicate that many agricultural technology contracts grant extensive control over farm data to technology providers, often without farmers fully understanding the implications. In other cases farmers unknowingly sign away their rights through service agreements that contain complex or vague terms. 

Corporations often employ restrictive data practices to exert control over farmers, limiting their independence and forcing them into costly, unfair dependencies. A recent investigation by the Federal Trade Commission into John Deere’s data practices examined the company’s “right to repair” policies, specifically whether Deere’s restrictions on accessing repair information and software prevent farmers from fixing their own machinery, thereby forcing them to rely on authorized dealerships for repairs. Some Agricultural Tech Providers further restrict farmers’ ability to switch between digital service providers, limiting the farmers’ right to data portability and impeding farmers’ capacity to analyze or use their data elsewhere.

Some companies use farmer data to manipulate prices and profit from this manipulation without compensating the farmers. For example, in the U.S. Corn Belt, thousands of farmers transmit real-time crop yield data from harvesting equipment to a data repository. Despite agreements to the contrary, the repository misappropriated this data by selling early yield estimates to commodity traders.

Additionally, AI-driven risk assessments based on biased or unrepresentative datasets could lead to higher insurance premiums for certain farmers. Similarly, data-driven price discrimination could occur when suppliers use collected data to set different prices based on farm size or productivity potential.

How existing data privacy laws apply to and manage precision agriculture 

Existing data privacy laws play an important role in managing precision agriculture by establishing frameworks for data ownership, access, and security. However, these laws remain insufficient in addressing the sector’s complexities. 

For example, the General Data Protection Regulation (GDPR) in the EU applies when farm data includes personally identifiable information (PII). However, most agricultural data is classified as non-personal and falls outside the regulation’s scope. The GDPR also includes provisions for automated decision-making, requiring transparency and accountability in AI-driven analytics. Though at least 20 states in the U.S. have introduced comprehensive data privacy laws, data collected through precision farming may not necessarily be covered under these regulations. 

In response to regulatory gaps, industry-led initiatives have attempted to provide guidance. The American Farm Bureau Federation’s “Privacy and Security Principles” for Farm Data provide voluntary guidelines to farmer ownership of agricultural data, transparency in data collection, robust security measures by agribusinesses, and clear disclosure in data-sharing agreements. Additionally, the Ag Data Transparency Evaluator is another voluntary assessment designed to help U.S. farmers understand how their data will be used when adopting precision agriculture technologies. The responses that data collectors enter into the evaluator are then reviewed by an independent third-party administrator, and companies that meet the criteria receive the Ag Data Transparent Seal, signaling their compliance with the American Farm Bureau’s Data Principles.

While these voluntary frameworks promote transparency, they lack enforcement mechanisms. Until formal legal frameworks catch up with agricultural technology, the best practice for farmers remains to negotiate well-crafted contracts specifying data control, portability, sharing permissions, anonymization, deletion policies, consent requirements, policy change notifications, data usage restrictions, modification rights, and security measures.

In addition to these contractual safeguards, it is recommended that farmers enhance their digital literacy to better protect the sensitive data collected, stored, and used by emerging technologies. For example, organizations like COPA-COGECA in the EU offer training that helps farmers understand their rights and learn advanced techniques for data integration and analysis, ultimately enabling them to retain greater control over their data.

Conclusion

AI-driven precision agriculture holds immense potential to enhance productivity, sustainability, and efficiency in farming. However, the rapid expansion of AI in this sector also brings significant data privacy risks. Current data privacy laws, such as GDPR and CCPA, provide some level of protection but fail to fully address the complexities of farm data management, leaving farmers vulnerable to unfair data-sharing agreements and monopolistic practices. While voluntary industry initiatives like the Ag Data Transparent Certification promote transparency, stronger legal frameworks are needed to ensure fair data-sharing practices. As AI continues to transform agriculture, policymakers, industry leaders, and farmers must work together to create a regulatory environment that protects farmer autonomy while fostering innovation and technological progress. 

#WJLTA #agriculturaltech #dataprivacy #AI #precisionagriculture

By: Lezlee Zapatka

Artificial intelligence (AI) is transforming the creative landscape, generating everything from music to literature, and even political cartoons. As AI-created art becomes more prevalent, legal and constitutional questions arise: Should AI-generated political speech be protected under the First Amendment? Or is it merely software output, lacking the expressive intent required for constitutional protection? These questions challenge traditional notions of speech, authorship, and legal personhood in the digital age.

The First Amendment and Political Speech

The First Amendment to the U.S. Constitution protects freedom of speech, including political expression. Historically, courts have extended these protections broadly to individuals, corporations – such as in Citizens United v. FEC, where the majority maintained that political speech is indispensable to a democracy, which is no less true because the speech comes from a corporation – and even symbolic speech, like burning the American flag (Texas v. Johnson). However, these protections have always been tied to human expression. The legal system has yet to determine whether an AI-generated work can be considered “speech” with constitutional protections.

AI as a Creator: The Copyright Perspective

The debate over AI-generated speech parallels discussions in copyright law. In Thaler v. Perlmutter, the Court’s analysis consideredthe definition of “authors” in copyright law, finding that the term is not explicitly defined in the Copyright Act or the Constitution. The Court reaffirmed that works must have human authorship to be eligible for copyright protection. This ruling suggests that AI-created works are not legally recognized as protectable expressions under existing intellectual property laws. If AI cannot be an author under copyright law, can it be a speaker under constitutional law?

The Argument for Protecting AI-Generated Political Art

Proponents of AI-generated speech protections argue that the First Amendment is designed to safeguard ideas and expression, not just speakers. Therefore, if an AI-generated political cartoon conveys a powerful message, does its origin matter? The freedom to publish anonymously is protected by the First Amendment (McIntyre v. Ohio Elections Comm’n), and in some cases, it protects speech with no identifiable author.

Consider an AI that generates satirical images critiquing a government policy. If the government attempts to suppress these images, should it be allowed to do so simply because no human created them? It could be argued that the consumer, not the creator, determines the value of speech. If an audience perceives an AI-generated cartoon as meaningful political expression, it may warrant protection regardless of authorship.

The Argument Against AI Speech Rights

Opponents counter that constitutional rights have always been tied to legal personhood. AI lacks independent thought, consciousness, and intent and instead feeds on data, i.e., content, which in turn fuels the algorithms and statistical models driving automation practices. Unlike human artists who create political cartoons with specific viewpoints, AI operates based on algorithms and training data, with no genuine intent to communicate a message.

Furthermore, granting AI First Amendment protections could create regulatory loopholes. For example, corporations or governments could deploy AI-generated disinformation under the guise of protected speech, making accountability difficult. Without a clear legal framework, AI-generated political content could challenge existing laws on election interference, defamation, and propaganda.

The Role of the Human Operator

A potential middle ground focuses on the intent of the human deploying the AI. If a person prompts an AI to generate a political cartoon, should that person’s First Amendment rights extend to the output? Courts could apply existing free speech protections to the human user while excluding the AI itself from direct constitutional recognition. This approach aligns with copyright law, where AI-assisted works can still receive protection if a human sufficiently contributes to the creative process.

Future Legal Considerations

As AI technology advances, courts and legislators will need to address these complex questions. Potential legal reforms may include:

• Clarifying AI Speech Protections: Should AI-generated content be treated like corporate speech, requiring a human intermediary, or should it remain unprotected?
• Regulating AI-Generated Political Content: If AI can autonomously create political messaging, should election laws and disinformation policies apply?
• Defining AI’s Role in Expression: Should AI be recognized as a tool of expression rather than a speaker, with First Amendment rights granted only to human operators?

Conclusion

AI-generated political cartoons sit at the crossroads of free speech, authorship, and emerging technology. While the First Amendment robustly protects political expression, it has always been tied to human speakers. While there are no current cases debating the issue, the No Artificial Intelligence Fake Replicas And Unauthorized Duplications (No AI FRAUD) Act has been proposed to broadly protect people from unauthorized use of their own images and voices by defining these things as the intellectual property of each individual. As AI’s creative capabilities expand, courts and lawmakers must decide whether free speech protections should be extended to machine-generated content or remain firmly within the realm of human expression. Until then, the debate over AI and free speech will continue to challenge our understanding of rights in the digital age.

#AI #politicalcartoons #misinformation #censorship #freespeech