Author: National Law Forum

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The Generative AI Revolution: Key Legal Considerations for the Fashion & Retail Industry

For better or worse, generative artificial intelligence (AI) is already transforming the way we live and work. Retail and fashion companies that fail to embrace AI likely risk losing their current market share or, worse, going out of business altogether. This paradigm shift is existential, and businesses that recognize and leverage AI will gain a significant competitive advantage.

For instance, some of our clients are using AI to streamline product design processes, reducing the costs and time necessary to generate designs, while others employ virtual models to circumvent issues related to adult and child modeling. Additionally, AI can provide valuable market intelligence to inform sales and distribution strategies. This alert will address these benefits, as well as other significant commercial advantages, and delve into the legal risks associated with utilizing AI in the fashion and retail industry.

There are significant commercial advantages to using AI for retail and fashion companies, including:

1. Product Design

From fast fashion to luxury brands, AI is set to revolutionize the fashion and retail industry. It enables the generation of innovative designs by drawing inspiration from a designer’s existing works and incorporating the designer’s unique style into new creations. For instance, in March 2023, G-Star Raw created its first denim couture piece designed by AI. We also worked with a client who utilized an AI tool to analyze its footwear designs from the previous two years and generate new designs for 2024. Remarkably, the AI tool produced 50 designs in just four minutes, with half of them being accepted by the company. Typically, this process would have required numerous designers and taken months to complete. While it is unlikely that AI tools will entirely replace human designers, the cost savings and efficiency gained from using such technology are undeniable and should not be overlooked.

2. Virtual Models

2023 marks a groundbreaking year with the world’s first AI Fashion Week and the launch of AI-generated campaigns, such as Valentino’s Maison Valentino Essentials collection, which combined AI-generated models with actual product photography. Fashion companies allocate a significant portion of their budget to model selection and hiring, necessitating entire departments and grappling with legal concerns such as royalties, SAG, moral issues, and child labor. By leveraging AI tools to create lifelike virtual models, these companies can eliminate the associated challenges and expenses, as AI models are not subject to labor laws — including child entertainment regulations — or collective bargaining agreements.

3. Advertising Campaigns

AI can also be used to create entire advertising campaigns from print copy to email blasts, blog posts, and social media. Companies traditionally invest substantial time and resources in these efforts, but AI can generate such content in mere moments. While human involvement remains essential, AI allows businesses to reduce the manpower required. Retailers can also benefit from AI-powered chatbots, which provide 24/7 customer support while reducing overhead expenses linked to in-person customer service. Moreover, AI’s predictive capabilities enable businesses to anticipate trends across various demographics in real-time, driving customer engagement. By processing and analyzing vast amounts of consumer data and preferences, brands can create hyper-personalized and bespoke content, enhancing customer acquisition, engagement, and retention. Furthermore, AI facilitates mass content creation at an impressively low cost, making it an invaluable tool in today’s competitive market.

4. ESG – Virtual Mirrors and Apps

From an environmental, social, and corporate governance (ESG) standpoint, the use of AI-powered technology can eliminate the need for retail stores to carry excess inventory, thereby reducing online returns and exchanges. AI smart mirrors can enhance in-store experiences for shoppers by enabling them to virtually try on outfits in various sizes and colors. Furthermore, customers can now enjoy the virtual try-on experience from the comfort of their homes, as demonstrated by Amazon’s “Virtual Try-On for Shoes,” which allows users to visualize how selected shoes will appear on their feet using their smartphone cameras.

5. Product Distribution and Logistics

Fashion companies rely on their C-level executives to make informed predictions about product quantities, potential sales in specific markets or stores, and the styles that will perform best in each market. In terms of logistics, AI models can be employed to forecast a business’s future sales by analyzing historical inventory and sales data. This ability to anticipate supply chain requirements can lead to increased profits and support the industry’s initiatives to reduce waste.

Legal and Ethical Risks

Although AI has some major advantages, it also comes with a number of legal and ethical risks that should be considered, including:

1. Accuracy and Reliability

For all their well-deserved accolades and hype, generative AI tools remain a work in progress. Users, especially commercial enterprises, should never assume that AI-created works are accurate, non-infringing, or fit for commercial use. In fact, there have been numerous recorded instances in which generative AI tools have created works that arguably infringe the copyrights of existing works, make up facts, or cite phantom sources. It is also important to note that works created by generative AI may incorporate or display third-party trademarks or celebrity likenesses, which generally cannot be used for commercial purposes without appropriate rights or permissions. Like anything else, companies should carefully vet any content produced by generative AI before using it for commercial purposes.

2. Data Security and Confidentiality

Before utilizing generative AI tools, companies should consider whether the specific tools adhere to internal data security and confidentiality standards. Like any third-party software, the security and data processing practices for these tools vary. Some tools may store and use prompts and other information submitted by users. Other tools offer assurances that prompts and other information will be deleted or anonymized. Enterprise AI solutions, such as Azure’s OpenAI Service, can also potentially help reduce privacy and data security risks by offering access to popular tools like ChatGPT, DALL-E, Codex, and more within the data security and confidentiality parameters required by the enterprise.

Before authorizing the use of generative AI tools, organizations and their legal counsel should (i) carefully review the applicable terms of use, (ii) inquire about access to tools or features that may offer enhanced privacy, security, or confidentiality, and (iii) consider whether to limit or restrict access on company networks to any tools that do not satisfy company data security or confidentiality requirements.

3. Software Development and Open-Source Software

One of the most popular use cases for generative AI has been computer coding and software development. But the proliferation of AI tools like GitHub Copilot, as well as a pending lawsuit against its developers, has raised a number of questions for legal counsel about whether use of such tools could expose companies to legal claims or license obligations.

These concerns stem in part from the use of open-source code libraries in the data sets for Copilot and similar tools. While open-source code is generally freely available for use, that does not mean that it may be used without condition or limitation. In fact, open-source code licenses typically impose a variety of obligations on individuals and entities that incorporate open-source code into their works. This may include requiring an attribution notice in the derivative work, providing access to source code, and/or requiring that the derivative work be made available on the same terms as the open-source code.

Many companies, particularly those that develop valuable software products, cannot risk having open-source code inadvertently included in their proprietary products or inadvertently disclosing proprietary code through insecure generative AI coding tools. That said, some AI developers are now providing tools that allow coders to exclude AI-generated code that matches code in large public repositories (in other words, making sure the AI assistant is not directly copying other public code), which would reduce the likelihood of an infringement claim or inclusion of open-source code. As with other AI generated content, users should proceed cautiously, while carefully reviewing and testing AI-contributed code.

4. Content Creation and Fair Compensation

In a recent interview, Billy Corgan, the lead singer of Smashing Pumpkins, predicted that “AI will change music forever” because once young artists figure out they can use generative AI tools to create new music, they won’t spend 10,000 hours in a basement the way he did. The same could be said for photography, visual art, writing, and other forms of creative expression.

This challenge to the notion of human authorship has ethical and legal implications. For example, generative AI tools have the potential to significantly undermine the IP royalty and licensing regimes that are intended to ensure human creators are fairly compensated for their work. Consider the recent example of the viral song, “Heart on My Sleeve,” which sounded like a collaboration between Drake and the Weeknd, but was in fact created entirely by AI. Before being removed from streaming services, the song racked up millions of plays — potentially depriving the real artists of royalties they would otherwise have earned from plays of their copyrighted songs. In response, some have suggested that human artists should be compensated when generative AI tools create works that mimic or are closely inspired by copyrighted works and/or that artists should be compensated if their works are used to train the large language models that make generative AI possible. Others have suggested that works should be clearly labeled if they are created by generative AI, so as to distinguish works created by humans from those created by machine.

5. Intellectual Property Protection and Enforcement

Content produced without significant human control and involvement is not protectable by US copyright or patent laws, creating a new orphan class of works with no human author and potentially no usage restrictions. That said, one key principle can go a long way to mitigating IP risk: generative AI tools should aid human creation, not replace it. Provided that generative AI tools are used merely to help with drafting or the creative process, then it is more likely that the resulting work product will be protectable under copyright or patent laws. In contrast, asking generative AI tools to create a finished work product, such as asking it to draft an entire legal brief, will likely deprive the final work product of protection under IP laws, not to mention the professional responsibility and ethical implications.

6. Labor and Employment

When Hollywood writers went on strike, one issue in particular generated headlines: a demand by the union to regulate the use of artificial intelligence on union projects, including prohibiting AI from writing or re-writing literary material; prohibiting its use as source material; and prohibiting the use of union content to train AI large language models. These demands are likely to presage future battles to maintain the primacy of human labor over cheaper or more efficient AI alternatives.

Employers are also utilizing automated systems to target job advertisements, recruit applicants, and make hiring decisions. Such systems expose employers to liability if they intentionally or unintentionally exclude or impact protected groups. According to the Equal Employment Opportunity Commission (EEOC), that’s precisely what happened with iTutorGroup, Inc.

7. Future Regulation

Earlier this year, Italy became the first Western country to ban ChatGPT, but it may not be the last. In the United States, legislators and prominent industry voices have called for proactive federal regulation, including the creation of a new federal agency that would be responsible for evaluating and licensing new AI technology. Others have suggested creating a federal private right of action that would make it easier for consumers to sue AI developers for harm they create. Whether US legislators and regulators can overcome partisan divisions and enact a comprehensive framework seems unlikely, but as is becoming increasingly clear, these are unprecedented times.

© 2023 ArentFox Schiff LLP
For more articles on AI, visit the NLR Communications, Media and Internet section.

The Supreme Court to Further Clarify “Transportation Worker” Exemption to the FAA

On September 29, 2023, the U.S. Supreme Court granted certiorari in Bissonnette v. LePage Bakeries Park St. LLC, a case from the Second Circuit Court of Appeals involving application of the Federal Arbitration Act’s (“FAA”) exemption for transportation workers.

Specifically, Section 1 of the FAA exempts from arbitration “contracts of employment of seamen, railroad employees, or any other class of workers engaged in foreign or interstate commerce”—the third category commonly referred to as the “transportation worker” exemption.

In the case below, the plaintiffs—a group of delivery drivers for a bakery—filed various wage and hour claims against the defendant, whom they claimed was their employer.  When the defendant moved to compel arbitration, the plaintiffs argued that, as bakery delivery drivers, they were exempt from arbitration as a “class of workers engaged in foreign or interstate commerce.”

The Second Circuit concluded that the plaintiffs were not exempt from arbitration because they were in the bakery industry, not in the transportation industry.  Therefore, the Second Circuit concluded that the plaintiffs were not transportation workers subject to exemption under Section 1 of the FAA. The Second Circuit’s decision turned, in part, on the interpretation of the U.S. Supreme Court’s decision in Saxon—a case that we previously reported on from last term.

In the Saxon case, the U.S. Supreme Court unanimously held that a ramp supervisor who frequently handled cargo for an interstate airline company was exempt under Section 1 of the FAA as a transportation worker.  In reaching that conclusion, the U.S. Supreme Court’s analysis focused on the “actual work” the worker performed, rather than the industry in which the employer operated—holding that “[the worker] is . . . a member of a ‘class of workers’ based on what she does at Southwest, not what Southwest does generally.”

Though the Second Circuit in Bissonnette acknowledged Saxon, the Second Circuit, in a split decision, held that Saxon did not come into play, stating that “those who work in the bakery industry are not transportation workers, even those who drive a truck from which they sell and deliver the breads and cakes”—essentially establishing a threshold requirement that the individual work in the “transportation industry” in order to be covered by the exemption.

In a pointed dissent, Judge Pooler wrote: “Of course these truckers are transportation workers,” and, “[b]y focusing on the nature of the defendants’ business, and not on the nature of the plaintiffs’ work, the majority offers the sort of industrywide approach Saxon proscribes.”

The U.S. Supreme Court’s forthcoming decision will likely clarify whether the FAA’s exemption contains an industry requirement or whether the analysis turns purely on the nature of the work the individual worker performs without regard to the underlying industry in which they work.  Regardless of the outcome, the U.S. Supreme Court’s decision will provide much-needed guidance at a time when more and more businesses are bringing transportation services in-house—opting to ship and deliver their own products as opposed to relying exclusively on traditional transportation companies.

Copyright © 2023, Hunton Andrews Kurth LLP. All Rights Reserved.
For more articles on employment, visit the NLR labor and employment section.

Navigating Data Ownership in the AI Age, Part 1: Types of Big Data and AI-Derived Data

The emergence of big data, artificial intelligence (AI), and the Internet of Things (IoT) has fundamentally transformed our understanding and utilization of data. While the value of big data is beyond dispute, its management introduces intricate legal questions, particularly concerning data ownership, licensing, and the protection of derived data. This article, the first installment in a two-part series, outlines challenges and opportunities presented by AI-processed and IoT-generated data. The second part, to be published Thursday, October 19, will discuss the complexities of the legal frameworks that govern data ownership.

Defining Big Data and Its Legal Implications

Big data serves as a comprehensive term for large, dynamically evolving collections of electronic data that often exceed the capabilities of traditional data management systems. This data is not merely voluminous but also possesses two key attributes with significant legal ramifications. First, big data is a valuable asset that can be leveraged for a multitude of applications, ranging from decoding consumer preferences to forecasting macroeconomic trends and identifying public health patterns. Second, the richness of big data often means it contains sensitive and confidential information, such as proprietary business intelligence and personally identifiable information (PII). As a result, the management and utilization of big data require stringent legal safeguards to ensure both the security and ethical handling of this information.

Legal Frameworks Governing Data Ownership

Navigating the intricate landscape of data ownership necessitates a multi-dimensional understanding that encompasses legal, ethical, and technological considerations. This complexity is further heightened by diverse intellectual property (IP) laws and trade secret statutes, each of which can confer exclusive rights over specific data sets. Additionally, jurisdictional variations in data protection laws, such as the European Union’s General Data Protection Regulation (GDPR) and the United States’ California Consumer Privacy Act (CCPA), introduce another layer of complexity. These laws empower individuals with greater control over their personal data, granting them the right to access, correct, delete, or port their information. However, the concept of “ownership” often varies depending on the jurisdiction and the type of data involved — be it personal or anonymized.

Machine-Generated Data and Ownership

The issue of data ownership extends beyond individual data to include machine-generated data, which introduces its own set of complexities. Whether it’s smart assistants generating data based on human interaction or autonomous vehicles operating independently of human input, ownership often resides with the entity that owns or operates the machine. This is typically defined by terms of service or end-user license agreements (EULAs). Moreover, IP laws, including patents and trade secrets, can also come into play, especially when the data undergoes specialized processing or analysis.

Derived Data and Algorithms

Derived and derivative algorithms refer to computational models or methods that evolve from, adapt, or draw inspiration from pre-existing algorithms. These new algorithms must introduce innovative functionalities, optimizations, or applications to be considered derived or derivative. Under U.S. copyright law, the creator of a derivative work generally holds the copyright for the new elements that did not exist in the original work. However, this does not extend to the foundational algorithm upon which the derivative algorithm is based. The ownership of the original algorithm remains with its initial creator unless explicitly transferred through legal means such as a licensing agreement.

In the field of patent law, derivative algorithms could potentially be patented if they meet the criteria of being new, non-obvious, and useful. However, the patent would only cover the novel aspects of the derivative algorithm, not the foundational algorithm from which it was derived. The original algorithm’s patent holder retains their rights, and any use of the derivative algorithm that employs the original algorithm’s patented aspects would require permission or licensing from the original patent holder.

Derived and derivative algorithms may also be subject to trade secret protection, which safeguards confidential information that provides a competitive advantage to its owner. Unlike patents, trade secrets do not require registration or public disclosure but do necessitate reasonable measures to maintain secrecy. For example, a company may employ non-disclosure agreements, encryption, or physical security measures to protect its proprietary algorithms.

AI-Processed and Derived Data

The advent of AI has ushered in a new era of data analytics, presenting both unique opportunities and challenges in the domain of IP rights. AI’s ability to generate “derived data” or “usage data” has far-reaching implications that intersect with multiple legal frameworks, including copyright, trade secrets, and potentially even patent law. This intersectionality adds a layer of complexity to the issue of data ownership, underscoring the critical need for explicit contractual clarity in licensing agreements and Data Use Agreements (DUAs).

AI-processed and derived data can manifest in various forms, each with unique characteristics. Extracted data refers to data culled from larger datasets for specific analyses. Restructured data has been reformatted or reorganized to facilitate more straightforward analysis. Augmented data is enriched with additional variables or parameters to provide a more comprehensive view. Inferred data involves the creation of new variables or insights based on the analysis of existing data. Lastly, modeled data has been transformed through ML models to predict future outcomes or trends. Importantly, these data types often contain new information or insights not present in the original dataset, thereby adding multiple layers of value and utility.

The benefits of using AI-processed and derived data can be encapsulated in three main points. First, AI algorithms can clean, sort, and enrich data, enhancing its quality. Second, the insights generated by AI can add significant value to the original data, rendering it more useful for various applications. Third, AI-processed data can catalyze new research, innovation, and product development avenues.

Conversely, the challenges in data ownership are multifaceted. First, AI-processed and derived data often involves a complex web of multiple stakeholders, including data providers, AI developers, and end users, which can complicate the determination of ownership rights. Second, the rapidly evolving landscape of AI and data science leads to a lack of clear definitions for terms like “derived data,” thereby introducing potential ambiguities in legal agreements. Third, given the involvement of multiple parties, it becomes imperative to establish clear and consistent definitions and agreements that meticulously outline the rights and responsibilities of each stakeholder.

© 2023 Bradley Arant Boult Cummings LLP
For more articles on AI, visit the NLR Communications, Media and Internet section.

Understanding Domain and IP Reputation in Email Deliverability

If you’re in the legal field, you’re well acquainted with the ways in which a good (or bad!) reputation can have an enormous impact on your practice’s success. Email deliverability is no different; mailbox providers (MBPs) use a variety of factors to determine what kind of reputation should be associated with your emails. Let’s break it down:

IP Reputation

An IP address is like a home address for your computer on the internet or local network, ensuring data sent from your computer reaches the correct destination and that data sent to you arrives at your computer.

When it comes to email, the IP address from which it originates is not just a technical detail—it carries a distinct reputation with it, much like a credit score. Just as lenders use credit scores to gauge the financial reliability of a person, MBPs evaluate an IP’s history to determine its trustworthiness. This reputation is shaped by different variables including:

  • Email volume and its consistency (or lack thereof)
  • Frequency of those emails being marked as spam
  • Bounce rates

If an IP address consistently sends out high-quality, relevant emails that recipients engage with, it’s much more likely to enjoy a positive reputation. On the flip side, its reputation can quickly plummet if it becomes associated with behaviors such as:

  • Sending large quantities of unsolicited email
  • High bounce rates
  • High frequency of spam complaints

A damaged IP reputation can have significant consequences that lead Email Service Providers (ESPs) to filter or block emails from that IP. This affects the sender’s ability to reach their intended audience effectively.

Domain Reputation

A domain, often recognized as a website or the web address, is a unique name that identifies a website or email address on the internet.

Every domain that sends email carries its own reputation, akin to a business’s standing in the community. This reputation is shaped by various behaviors and practices associated with the email you send:

  • Engagement
    • Mailbox providers want to see that your subscribers are engaged
    • They rely on hundreds of different signals to filter email, but engagement is heavily weighted.
    • Any time your subscribers show strong interest or engagement in your content, it’s a big win for your overall deliverability
  • Permission
    • Sending unsolicited email is a surefire way to harm your domain’s reputation
    • Unsolicited email is highly likely to result in spam complaints or even a spamtrap hit
    • Any domain associated with large numbers of spam complaints raises serious alarms for mailbox providers
  • Bounces
    • Large numbers of bounces can decrease trust in your domain
    • Because of this, it’s important to regularly curate and update your email lists Sending emails to old, unengaged, or invalid addresses often results in high bounce rates
    • List hygiene practices such as removing inactive subscribers or those who haven’t engaged in a long time are an effective preventative measure
  • History
    • Your domain’s email-sending history plays a significant role in its reputation
    • A consistent track record of sending high-quality, engaging emails can enhance your domain’s standing while any past transgressions, like sending to purchased lists or being flagged by spam traps, can linger and affect future deliverability
    • MBPs have a long memory, so it’s important to avoid these problems wherever possible
  • Authentication
    • Many inbox providers won’t accept your mail if it isn’t able to pass email authentication protocols like SPF (Sender Policy Framework) and DKIM (DomainKeys Identified Mail)
    • If you have a sending domain validated within Lawmatics, SPF and DKIM are automatically implemented when you add our CNAME records to your domain
    • However, if you remove or alter those records, there’s a good chance that authentication will fail and your mail will bounce
  • Blocklists
    • Having your domain listed on a blocklist can have a major effect on your deliverability
    • That being said, it’s worth noting that anyone can create a blocklist and not all of them are reputable or affect your email delivery

Ultimately, you and your firm play a major role in your domain’s reputation. Being mindful of the content you send, maintaining updated email lists, and engaging with your subscribers in a meaningful way aren’t just strategies to maintain a strong domain reputation; they’re essential steps to elevate your firm’s credibility. They also serve to enhance client engagement and secure consistent deliverability for your communications.

©2023 — Lawmatics
Article by Shay Paris of Lawmatics
For more articles on legal marketing, visit the NLR Law Office Management section.

US Halts Visa Services in Israel, Focuses on Assisting US Citizens

The United States has halted immigrant and nonimmigrant visa services in Israel amid ongoing security concerns.

‌‌Key Points:

  • Visa services are unavailable at this time at the U.S Embassy in Jerusalem or the Embassy Branch Office in Tel Aviv. Non-U.S. citizens in need of emergency visa services should request an expedited appointment at a U.S. embassy or consulate other than Jerusalem or Tel Aviv.
  • U.S. citizens in Israel, the West Bank or Gaza who would like assistance should fill out this crisis intake form, which allows the U.S. State Department to respond to requests from evacuees in leaving or obtaining other routine or emergency passport or citizen services or information.
  • Commercial flight availability remains limited out of Ben Gurion Airport, but the U.S. government is facilitating charter flights and other modes of transportation for U.S. citizens. The State Department said these flights will continue until at least Oct. 19.
  • The Israeli government has extended the validity of work visas until Nov. 9, 2023, for all foreign nationals in the country whose Israeli visas will expire within the next month.
  • Up-to-date information is available on the Embassy’s News & Events and Travel Alerts pages.

BAL Analysis: Visa services are not available in Israel at this time. The situation continues to evolve and travel rules and procedures may change with little or no notice. U.S. citizens in Israel are encouraged to monitor State Department websites for updates.

©2023 Berry Appleman & Leiden. All Rights Reserved.

Business Immigration Could Be Impacted if Congress Fails to Fund Government Through FY 2024

On September 30, 2023, President Joe Biden signed into law stopgap funding legislation that temporarily averted a government shutdown. The legislation, which passed the U.S. Congress with bipartisan support and extended funding for the federal government for a period of forty-five days, will keep the government running through November 17, 2023.

Quick Hits

  • A recently enacted stopgap funding measure has allowed the government to continue operations, including immigration services, through November 17, 2023.
  • If Congress cannot reach an agreement to fund the federal government before November 17, 2023, a partial government shutdown may occur.
  • A government shutdown would disrupt federal agencies that are responsible for immigration-related services and benefits. U.S. Citizenship and Immigration Services (USCIS) is a fee-generating agency; during past government shutdowns, USCIS offices generally continued to operate.
  • The U.S. Department of Labor (DOL) is not fee generating, and, consequently, the department’s operations were significantly hindered during previous government shutdowns. As a result, any immigration petition that requires a DOL pre-filing will likely be impacted.

The most significant business immigration impacts of a government shutdown on U.S. employers may include:

  • the DOL taking the Foreign Labor Application Gateway (FLAG) system offline, resulting in a suspension of new labor condition applications (LCAs) that are required for H-1B, H-1B1, and E-3 nonimmigrant petitions;
    • a DOL suspension of PERM labor certifications and prevailing wage determinations (PWD), which would further extend already lengthy PERM and PWD processing times; and
    • possible visa processing delays at U.S. consulates. While the U.S. Department of State is partially funded by visa application fees, it is possible that nonemergency services could be suspended during a prolonged shutdown.

Next Steps

While Congress temporarily averted a government shutdown, the members of the U.S. House of Representatives and the U.S. Senate have not reached an agreement on an appropriations bill to fund the federal government through the entirety of fiscal year (FY) 2024. The risk of a government shutdown remains if Congress is not able to resolve spending and policy disagreements prior to November 17, 2023.

© 2023, Ogletree, Deakins, Nash, Smoak & Stewart, P.C., All Rights Reserved.
Article by  Katie W. Desmond of Ogletree, Deakins, Nash, Smoak & Stewart, P.C.
For more articles on business immigration, visit the NLR Immigration section.

AI Versus Westlaw Copyright Bellwether Hurtles Toward Jury as Summary Judgment Largely Denied

In one of the first lawsuits to allege that generative AI companies violate the U.S. Copyright Act by using copyrighted works to train machine learning models, Judge Stephanos Bibas of the Delaware Circuit Court recently denied the majority of issues raised in cross motions for summary judgment filed by plaintiff Thomson Reuters and defendant Ross Intelligence Inc.  The court declined to issue a dispositive ruling on the hot-button question of whether the fair use doctrine protects generative AI companies that use copyrighted materials to train their programs.

Thomson Reuters (owner of Westlaw) sued Ross Intelligence, a legal-research generative AI startup, in May 2020, alleging that Ross was liable for both copyright infringement and tortious interference with contract.  The allegations against Ross stem from its endeavor to create a search engine that uses machine learning and artificial intelligence to provide answers to commonly asked legal questions.

In need of material to train its generative AI, Ross attempted to obtain a license to use Westlaw.  When Westlaw turned Ross away, it asked third-party legal research companies to provide it with legal material — much of which those legal research companies obtained from Westlaw.  Thomson Reuters contends that Ross copied large portions of Westlaw’s Headnotes and Key Number System.

After Ross’s motion to dismiss the copyright claim was denied in March of 2021, the parties each moved for summary judgment on a multitude of issues.  Most notably, Thomson Reuters moved for summary judgment on its copyright infringement claim, and both sides moved for summary judgment on Ross’s assertion of fair use.

On the issue of copyright infringement, Judge Bibas granted Thomson Reuters’ motion on the limited issue that Ross “copied at least portions of” Westlaw’s work.  However, the remaining issues of the copyright claim — the validity of Thomson Reuters’ copyright and the substantial similarity of Ross’s work — were denied summary judgment and will go to a jury.

On the issue of fair use, Ross contends that its use of Thomson Reuters’ materials, even if found to be copyright protected, was permissible.

The question of fair use protection for generative AI developers is significant because all generative AI requires the input of a vast amount of information to train its machine learning and develop its content.  Intellectual property law comes into play where the training materials — the “input” into the AI — are copyright protected.  When the input material is copyright protected, AI developers may seek to rely on the fair use doctrine to use copyright-protected works without permission from the copyright holder.

As discussed in the court’s opinion, whether the use of copyrighted material is fair depends on the balance of four factors — the purpose and character of the use, the nature of the copyrighted work, the amount and substantiality of the portion used in relation to the copyrighted work as a whole, and the effect of the use upon the potential market for the copyrighted work.  Courts tend to give the most weight to the first and fourth factors.

The first factor, the purpose and character of the use, looks to the commerciality and transformativeness of the use of the copyrighted work.  While Judge Bibas held that Ross’s use of Thomson Reuters’ materials was undoubtedly commercial in nature, which weighs against finding fair use, the court could not say as a matter of law whether Ross’ works were sufficiently transformative.  Each party offers a differing account of exactly how Ross used the Westlaw information — did Ross merely translate Westlaw’s headnotes into numerical data that would later be displayed by its AI search engine?  Or did it, as Ross contends, study Westlaw’s headnotes and opinion quotes only to analyze language patterns rather than replicate Westlaw’s protected expressions?

According to the court, the answers to these questions fall within the discretion of a jury. In this regard, the court noted that Ross’s use was “transformative intermediate copying if Ross’s AI only studied the language patterns in the headnotes to learn how to produce judicial opinion quotes.  But if Thomson Reuters is right that Ross used the untransformed text of its headnotes to get its AI to replicate and reproduce the creative drafting done by Westlaw’s attorney editors,” then Ross’s argument that its work was sufficiently transformative might fail.

As to the other three factors for fair use, the court similarly held that they could not be resolved on summary judgment because of remaining questions of fact.  However, the court noted that the second factor — the nature of Thomson Reuters’ copyrighted work — seemed to favor fair use.  Specifically, Westlaw’s Key Number system is a method of organization that “inherently involves significantly less creative or original expression” than traditionally protected materials, and the Headnotes are “akin to news reporting” that must be carefully separated from the unprotected underlying facts of the judicial opinions they synthesize. A jury trial in this case might yield the first judgment on issues related to generative AI, copyright, and fair use.  This case could have an impact not only on the AI and machine learning industry, but also the public interest as a whole while the world continues to adjust to the myriad new realities and resulting issues of first impression on the new AI frontier.

© 2023 Vedder Price
Article by Keeley Sawyer of Vedder Price
For more articles on AI copyright, visit the NLR Intellectual Property law section.

California’s “Delete Act” Significantly Expands Requirements for Data Brokers

California recently passed a groundbreaking new law aimed at further regulating the data broker industry. California is already one of only three states (along with Oregon and Vermont) that require data brokers—businesses that collect and sell personal information from consumers with whom the business does not have a direct relationship—to meet certain registration requirements.

Under the new law, the regulation of data brokers—including the registration requirements—falls within the purview of the California Privacy Protection Agency (CPPA) and requires data brokers to comply with expanded disclosure and record keeping requirements. Notably, the law also requires the CPPA to make an “accessible deletion mechanism” available to consumers at no cost by January 1, 2026. The tool is intended to act as a single “delete button,” allowing consumers to request the deletion of all of their personal information held by registered data brokers within the state.

Putting it into practice: Businesses considered “data brokers” should carefully review the new and expanded requirements and develop a compliance plan, as certain aspects of the law (e.g., the enhanced registry requirements) go into effect as soon as January 31, 2024.

Copyright © 2023, Sheppard Mullin Richter & Hampton LLP.
Article by Brittany Walter of Sheppard, Mullin, Richter & Hampton LLP
For more articles on data brokers, visit the NLR Communications, Media and Internet section.

Chat with Caution: The Growing Data Privacy Compliance and Litigation Risk of Chatbots

In a new wave of privacy litigation, plaintiffs have recently filed dozens of class action lawsuits in state and federal courts, primarily in California, seeking damages for alleged “wiretapping” by companies with public-facing websites. The complaints assert a common theory: that website owners using chatbot functions to engage with customers are violating state wiretapping laws by recording chats and giving service providers access to them, which plaintiffs label “illegal eavesdropping.”

Chatbot wiretapping complaints seek substantial damages from defendants and assert new theories that would dramatically expand the application of state wiretapping laws to customer support functions on business websites.

Although there are compelling reasons why courts should decline to extend wiretapping liability to these contexts, early motions to dismiss have met mixed outcomes. As a result, businesses that use chatbot functions to support customers now face a high-risk litigation environment, with inconsistent court rulings to date, uncertain legal holdings ahead, significant statutory damages exposure, and a rapid uptick in plaintiff activity.

Strict State Wiretapping Laws

Massachusetts and California have some of the most restrictive wiretapping laws in the nation, requiring all parties to consent to a recording, in contrast to the one-party consent required under federal and many state laws. Those two states have been key battlegrounds for plaintiffs attempting to extend state privacy laws to website functions, partly because they provide for significant statutory damages per violation and an award of attorney’s fees.

Other states with wiretapping statutes requiring the consent of all parties include Delaware, Florida, Illinois, Maryland, Montana, Nevada, New Hampshire, Pennsylvania, and Washington. As in Massachusetts and California, litigants in Florida and Pennsylvania have started asserting wiretapping claims based on website functions.

Plaintiffs’ Efforts to Extend State Wiretapping Laws to Chatbot Functions

Chatbot litigation is a product of early favorable rulings in cases targeting other website technologies, refashioned to focus on chat functions. Chatbots allow users to direct inquiries to AI virtual assistants or human customer service representatives. Chatbot functions are often deployed using third-party vendor software, and when chat conversations are recorded, those vendors may be provided access to live recordings or transcripts.

This most recent wave of plaintiffs now claim that recording chat conversations and making them accessible to vendors violates state wiretapping laws, with liability for both the website operator and the vendor. However, there are several reasons why the application of wiretapping laws in this context is inappropriate, and defendants are asserting these legal arguments in early dispositive motion practice with mixed results.

What Businesses Can Do to Address Growing Chatbot Litigation Risk

Despite compelling legal arguments for why these suits should be stopped, businesses with website chat functions should exercise caution to avoid being targeted, as we expect to see chatbot wiretap claims to skyrocket. This litigation risk is present in all two-party consent states, but especially in Massachusetts and California. Companies should beware that they can be targeted in multiple states, even if they do not offer products or services directly to consumers.

In this environment, a review and update of your company’s website for data privacy compliance, including chatbot activities, is advisable to avoid expensive litigation. These measures include:

  • Incorporating clear disclosure language and robust affirmative consent procedures into the website’s chat functions, including specific notification in the function itself that the chatbot is recording and storing communications
  • Expanding website dispute resolution terms, including terms that could reduce the risk of class action litigation and mass arbitration
  • Updating the website’s privacy policy to accurately and clearly explain what data, if any, is recorded, stored, and transmitted to service providers through its chat functions, ideally in a dedicated “chat” section
  • Considering data minimization measures in connection with website chat functions
  • Evaluating third-party software vendors’ compliance history, including due diligence to ensure a complete understanding of how chatbot data is collected, transmitted, stored, and used, and whether the third party’s privacy policies are acceptable

Companies may also want to consider minimizing aspects of their chatbots that have a high annoyance factor – such as blinking “notifications” – to reduce the likelihood of attracting a suit. This list is not comprehensive, and businesses should ensure their legal teams are aware of their website functions and data collection practices.

©2023 Pierce Atwood LLP. All rights reserved.
For more articles on privacy, visit the NLR Communications, Media and Internet section.

FTC Junk Fee Ban Proposed Rule Released

The Federal Trade Commission (FTC) released a new proposed rule to ban junk fees, which are unexpected, hidden, and “bogus” charges that are often applied later in a transaction. The FTC announced the proposed rule on October 11, 2023 after receiving 12,000 comments from consumers about how these fees impact them. The FTC is currently “seeking a new round of comments on a proposed junk fee rule,” according to a press release issued by the agency.

Junk fees include charges added when purchasing concert tickets online, making hotel and resort reservations, changing airline booking and seat choice fees, paying utility bills and renting an apartment. Junk fees are sometimes called partitioned pricing, drip pricing or shrouded pricing, according to Ashish Pradhan of Cornerstone Research. Consumers told the FTC that sellers often don’t say what the fees are for, and if they’re getting anything in return for paying them.

“All too often, Americans are plagued with unexpected and unnecessary fees they can’t escape. These junk fees now cost Americans tens of billions of dollars per year—money that corporations are extracting from working families just because they can,”  FTC Chair Lina M. Khan stated today. “By hiding the total price, these junk fees make it harder for consumers to shop for the best product or service and punish businesses who are honest upfront. The FTC’s proposed rule to ban junk fees will save people money and time and make our markets fairer and more competitive.”

The FTC estimates that junk fees can result in “tens of billions of dollars per year in unexpected costs” for consumers, and more than 50 million hours of time spent searching for the total price of short-term lodging and tickets for live events per year.

What Is the FTC Junk Fee Proposed Rule?

The proposed rule requires businesses to include all mandatory fees to be disclosed in pricing and prohibits sellers from applying any hidden fees during the transaction. The FTC said that this would help consumers “know exactly how much they are paying and what they are getting and spur companies to compete on offering the lowest price.”

Specifically, the Junk Fee Proposed Rule bans:

  • Hidden fees. These fees drive up the price of purchases, often before the transaction is complete. The proposed rule also bars businesses from advertising prices that exclude or hide mandatory fees.
  • Bogus fees. The FTC said that companies often charge “bogus fees.” The agency characterizes these fees as charges that consumers are asked to pay without knowing what their purpose is. The proposed rule requires businesses to tell consumers what these fees are for, what the amount is up front and if the fees can be refunded.

The proposed rule allows the FTC to issue monetary penalties against noncompliant companies and provide refunds to affected consumers.

Junk Fee Regulatory Measures from Other Federal Agencies

The Federal Communications Commission (FCC)

The FTC isn’t the only federal agency targeting junk fees. Other federal agencies are also acting against a variety of add on fees. The Federal Communications Commission (FCC) started implementing its Broadband Consumer Labels tool aimed at increasing price transparency.

“No one likes surprise charges on their bill. Consumers deserve to know exactly what they are paying for when they sign up for communications services. But when it comes to these bills, what you see isn’t always what you get,” said FCC Chairwoman Jessica Rosenworcel on March 23, 2023. “Instead, consumers have often been saddled with additional junk fees that may exorbitantly raise the price of their previously agreed-to monthly charges. To combat this, we’re implementing Broadband Consumer Labels, a new tool that will increase price transparency and reduce cost confusion, help consumers compare services, and provide ‘all-in-pricing’ so that every American can understand upfront and without any surprises how much they can expect to be paying for these services.”

The Consumer Financial Protection Bureau (CFPB)

Additionally, in March, the Consumer Financial Protection Bureau (CFPB) released a report on the use of junk fees “in deposit accounts and in multiple loan servicing markets, including the auto, mortgage, student, and payday/small loan sectors,” according to Greenberg Traurig.

“Americans are fed up with the junk fees that are creeping across the economy,” said CFPB Director Rohit Chopra in the FTC press release. “The FTC’s proposed rule will protect families and honest businesses from race-to-the-bottom abuses that cost us billions of dollars each year. If finalized, the CFPB will enforce the rule against violators in the financial industry and ensure that these firms play fairly.”\

The Department of Housing and Urban Development (HUD)

The Biden Administration and the Department of Housing and Urban Development in a July 19, 2023, press release, specifically address add-on fees in rental housing. “Earlier this year, we called for reform in the housing industry to increase transparency for renters across the country, reflecting the Biden-Harris administration and the Department of Housing and Urban Development’s commitment,” said HUD Secretary Marcia L. Fudge in the FTC press release.

According to HUD rental application fees can be up to $100 or more per application, and, importantly, they often exceed the cost of conducting the background and credit checks. Given that prospective renters often apply for multiple units over the course of their housing search, these application fees can add up to hundreds of dollars.

The Department of Transportation (DOT)

The Department of Transportation in a March 2023 press release addressed aggravating airline fees: after DOT secured commitments from major U.S. airlines to provide free rebooking, meals, and hotels when they are responsible for stranding passengers. Dot stated that they were working to stop airlines from forcing parents to pay to sit next to their kids by requiring airlines to disclose hidden fees for things like extra bags. DOT stated that they helped secure billions of dollars in refunds for passengers whose flights are canceled.

In 2022, Secretary Buttigieg pressed U.S. airlines to do more for passengers who had a flight canceled or delayed because of the airline, by informing the CEOs of the 10 largest U.S. airlines that the DOT would publish a dashboard on amenities and services provided such as rebooking, meals, or hotels in the event of a controllable delay or cancellation. Prior to Buttigieg’s urging, none of the 10 largest U.S. airlines guaranteed meals or hotels when a delay or cancellation was within the airlines’ control, and only one offered free rebooking.   As of March 2023, all of the 10 largest U.S. airlines guarantee meals and rebooking, and nine guarantee hotels when an airline issue causes a cancellation or delay.

What’s Next?

Consumers can submit comments to the FTC electronically for 60 days once the notice of proposed rulemaking is published in the Federal Register. Consumers can also send written comments to the FTC—instructions on how to do this can be found in the Federal Register notice under the “Supplementary Information” section.

Copyright ©2023 National Law Forum, LLC
For more articles on the FTC, visit the NLR Antitrust and Trade section.