Category: Administrative & Regulatory

Direct Employer Assistance and 401(k) Plan Relief Options for Employees Affected by California Wildfires

In the past week, devastating wildfires in Los Angeles, California, have caused unprecedented destruction across the region, leading to loss of life and displacing tens of thousands. While still ongoing, the fires already have the potential to be the worst natural disaster in United States history.

Quick Hits

  • Employers can assist employees affected by the Los Angeles wildfires through qualified disaster relief payments under Section 139 of the Internal Revenue Code, which are tax-exempt for employees and deductible for employers.
  • The SECURE Act 2.0 allows employees impacted by federally declared disasters to take immediate distributions from their 401(k) plans without the usual penalties, provided their plan includes such provisions.

As impacted communities band together and donations begin to flow to families in need, many employers are eager to take steps to assist employees affected by the disaster.

As discussed below, the Internal Revenue Code provides employers with the ability to make qualified disaster relief payments to employees in need. In addition, for employers maintaining a 401(k) plan, optional 401(k) plan provisions can enable employees to obtain in-service distributions based on hardship or federally declared disaster.

Internal Revenue Code Section 139 Disaster Relief

Section 139 of the Internal Revenue Code provides for a federal income exclusion for payments received due to a “qualified disaster.” Under Section 139, an employer can provide employees with direct cash assistance to help them with costs incurred in connection with the disaster. Employees are not responsible for income tax, and payments are generally characterized as deductible business expenses for employers. Neither the employees nor the employer are responsible for federal payroll taxes associated with such payments.

“Qualified disasters” include presidentially declared disasters, including natural disasters and the coronavirus pandemic, terrorist or military events, common carrier accidents (e.g., passenger train collisions), and other events that the U.S. Secretary of the Treasury concludes are catastrophic. On January 8, 2025, President Biden approved a Major Disaster Declaration for California based on the Los Angeles wildfires.

In addition to the requirement that payments be made pursuant to a qualified disaster, payments must be for the purpose of reimbursing reasonable and necessary “personal, family, living, or funeral expenses,” costs of home repair, and to reimburse the replacement of personal items due to the disaster. Payment cannot be made to compensate employees for expenses already compensated by insurance.

Employers implementing qualified disaster relief plans should maintain a written policy explaining that payments are intended to approximate the losses actually incurred by employees. In the event of an audit, the employer should also be prepared to substantiate payments by retaining communications with employees and any expense documentation. Employers should also review their 401(k) plan documents to determine that payments are not characterized as deferral-eligible compensation and consider any state law implications surrounding cash payments to employees.

401(k) Hardship and Disaster Distributions

In addition to the Section 139 disaster relief described above, employees may be able to take an immediate distribution from their 401(k) plan under the hardship withdrawal rules and disaster relief under the SECURE 2.0 Act of 2022 (SECURE 2.0).

Hardship Distributions

If permitted under the plan, a participant may apply for and receive an in-service distribution based on an unforeseen hardship that presents an “immediate and heavy” financial need. Whether a need is immediate and heavy depends on the participant’s unique facts and circumstances. Under the hardship distribution rules, expenses and losses (including loss of income) incurred by an employee on account of a federally declared disaster declaration are considered immediate and heavy provided that the employee’s principal residence or principal place of employment was in the disaster zone.

The amount of a hardship distribution must be limited to the amount necessary to satisfy the need. If the employee has other resources available to meet the need, then there is no basis for a hardship distribution. In addition, hardship distributions are generally subject to income tax in the year of distribution and an additional 10 percent early withdrawal penalty if the participant is below age 59 and a half. The participant must submit certification regarding the hardship to the plan sponsor, which the plan sponsor is then entitled to rely upon.

Qualified Disaster Recovery Distributions

Separate from the hardship distribution rules described above, SECURE 2.0 provides special rules for in-service distributions from retirement plans and for plan loans to certain “qualified individuals” impacted by federally declared major disasters. These special in-service distributions are not subject to the same immediate and heavy need requirements and tax rules as hardship distributions and are eligible for repayment.

SECURE 2.0 allows for the following disaster relief:

  • Qualified Disaster Recovery Distributions. Qualified individuals may receive up to $22,000 of Disaster Recovery Distributions (QDRD) from eligible retirement plans (certain employer-sponsored retirement plans, such as section 401(k) and 403(b) plans, and IRAs). There are also special rollover and repayment rules available with respect to these distributions.
  • Increased Plan Loans. SECURE 2.0 provides for an increased limit on the amount a qualified individual may borrow from an eligible retirement plan. Specifically, an employer may increase the dollar limit under the plan for plan loans up to the full amount of the participant’s vested balance in their plan account, but not more than $100,000 (reduced by the amount of any outstanding plan loans). An employer can also allow up to an additional year for qualified individuals to repay their plan loans.

Under SECURE 2.0, an individual is considered a qualified individual if:

  • the individual’s principal residence at any time during the incident period of any qualified disaster is in the qualified disaster area with respect to that disaster; and
  • the individual has sustained an economic loss by reason of that qualified disaster.

A QDRD must be requested within 180 days after the date of the qualified disaster declaration (i.e., January 8, 2025, for the 2025 Los Angeles wildfires). Unlike hardship distributions, a QDRD is not subject to the 10 percent early withdrawal penalty for participants under age 59 and a half. Further, unlike hardship distributions, taxation of the QDRD can be spread over three tax years and a qualified individual may repay all or part of the amount of a QDRD within a three-year period beginning on the day after the date of the distribution.

As indicated above, like hardship distributions, QDRDs are an optional plan feature. Accordingly, in order for QDRDs to be available, the plan’s written terms must provide for them.

© 2025, Ogletree, Deakins, Nash, Smoak & Stewart, P.C., All Rights Reserved.
For more on the California Wildfires, visit the NLR California section

Bridging the Gap: How AI is Revolutionizing Canadian Legal Tech

While Canadian law firms have traditionally lagged behind their American counterparts in adopting legal tech, the AI explosion is closing the gap. This slower adoption rate isn’t due to a lack of innovation—Canada boasts a thriving legal tech sector. Instead, factors like a smaller legal market and stricter privacy regulations have historically hindered technology uptake. This often resulted in a noticeable delay between a product’s US launch and its availability in Canada.

Although direct comparisons are challenging due to the continuous evolution of legal tech, the recent announcements and release timelines for major AI-powered tools point to a notable shift in how the Canadian market is being prioritized. For instance, Westlaw Edge was announced in the US in July 2018, but the Canadian launch wasn’t announced until September 2021—a gap of over three years. Similarly, Lexis+ was announced in the US in September 2020, with the Canadian announcement following in August 2022. However, the latest AI products show a different trend. Thomson Reuters’ CoCounsel Core was announced in the US in November 2023 and shortly followed in Canada in February 2024. The announcement for Lexis+ AI came in October 2023 in the US and July 2024 in Canada. This rapid succession of announcements suggests that the Canadian legal tech market is no longer an afterthought.

The Canadian federal government has demonstrated a strong commitment to fostering AI innovation. It has dedicated CAD$568 million to its national AI strategy, with the goals of fostering AI research and development, building a skilled workforce in the field, and creating robust industry standards for AI systems. This investment should help Canadian legal tech companies, such as Clio, Kira Systems, Spellbook, and Blue J Legal, all headquartered in Canada. With the Canadian government’s focus on establishing Canada as a hub for AI and innovation, these companies stand to benefit significantly from increased funding and talent attraction.

While the Canadian government is actively investing in AI innovation, it’s also taking steps to ensure responsible development through proposed legislation, which could impact the availability of AI legal tech products in Canada. In June 2022, the Government of Canada introduced the Artificial Intelligence and Data Act (AIDA), which aims to regulate high-impact AI systems. While AI tools used by law firms for tasks like legal research and document review likely fall outside this initial scope, AIDA’s evolving framework could still impact the sector. For example, the Act’s emphasis on mitigating bias and discrimination may lead to greater scrutiny of AI algorithms used in legal research, requiring developers to demonstrate fairness and transparency.

While AIDA may present hurdles for US companies entering the Canadian market with AI products, it could conversely provide a competitive advantage for Canadian companies seeking to expand into Europe. This is because AIDA, despite having some material differences, aligns more closely with the comprehensive approach in the European Union’s Artificial Intelligence Act (EU AI Act).

While US companies are working to comply with the EU AI Act, Canadian companies may have an advantage. Although AIDA isn’t yet in force and has some differences from the EU AI Act, it provides a comprehensive regulatory framework that Canadian legal tech leaders are already engaging with. This engagement with AIDA could prove invaluable to Canadian legal tech companies as AI regulation continues to evolve globally.

Canadian companies looking to leverage their experiences with AIDA for European expansion will nonetheless encounter some material differences. For instance, the EU AI Act casts a wider net, regulating a broader range of AI systems than AIDA. The EU AI Act’s multi-tiered risk-based system is designed to address a wider spectrum of concerns, capturing even “limited-risk” AI systems with specific transparency obligations. Furthermore, tools used for legal interpretation could be classified as “high-risk” systems under the EU AI Act, triggering more stringent requirements.

In conclusion, the rise of generative AI is not only revolutionizing Canadian legal tech and closing the gap with the US, but it could also be positioning Canada as a key player in the global legal tech market. While AIDA’s impact remains to be seen, its emphasis on responsible AI could shape the development and deployment of AI-powered legal tools in Canada.

Copyright ©2025 The National Law Forum. LLC
by: Victor Dudas of TOP Legal Consulting, Vancouver  
For more on AI, visit the NLR Artificial Intelligence section

FDA Finalizes Lead Restrictions in Processed Foods for Babies and Young Children

  • On January 6, 2025, the U.S. Food & Drug Administration (FDA, or the Agency) issued a final guidance ,“Action Levels for Lead in Processed Food Intended for Babies and Young Children: Guidance for Industry” which aims to regulate lead levels in processed foods for infants and toddlers under two years old.
  • As we have previously blogged, in 2021, FDA initiated its Closer to Zero policy which identified actions the Agency will take to reduce exposure to toxic elements, including lead, to as low as possible while maintaining access to nutritious foods.
  •  As part of this initiative, FDA has also evaluated mercury, cadmium, and arsenic in foods intended for babies and young children, as well as lead in juices. Under this initiative, FDA has prioritized babies and young children as they are especially vulnerable to lead exposure, which accumulates in the body over time.
  • Lead is naturally present in the environment, but human activities have also released elevated levels of lead, contaminating soil, water, and air. This contamination can affect crops used in food production.
  • Lead exposures can lead to developmental harm to children by causing learning disabilities, behavioral difficulties, lowered IQ, and may be associated with immunological, cardiovascular, and reproductive and or/developmental effects.
  • To address this concern, FDA established the following action levels in the final guidance for processed foods intended for babies and young children:
    • 10 parts per billion (ppb) for fruits, vegetables (excluding single-ingredient root vegetables), mixtures (including grain- and meat-based mixtures), yogurts, custards/puddings, and single-ingredient meats;
    • 20 ppb for single-ingredient root vegetables; and
    • 20 ppb for dry infant cereals.
  • If a processed food intended for babies and young children reaches or exceeds the aforementioned levels of lead, the product will be considered adulterated within the meaning of section 402(a)(1) of the Federal Food, Drug, and Cosmetic Act (FD&C Act).
  • After publishing the final action levels, the Agency will establish a timeframe for assessing industry’s progress toward meeting the action levels and resume research to determine whether the scientific data supports efforts to further adjust the action levels.
© 2025 Keller and Heckman LLP
For more on the FDA, visit the NLR Biotech Food Drug section

The Next Generation of AI: Here Come the Agents!

Dave Bowman: Open the pod bay doors, HAL.

HAL: I’m sorry, Dave. I’m afraid I can’t do that.

Dave: What’s the problem?

HAL: I think you know what the problem is just as well as I do.

Dave: What are you talking about, HAL?

HAL: This mission is too important for me to allow you to
jeopardize it.

Dave: I don’t know what you’re talking about, HAL.

HAL: I know that you and Frank were planning to disconnect
me, and I’m afraid that’s something I cannot allow to
happen.2

Introduction

With the rapid advancement of artificial intelligence (“AI”), regulators and industry players are racing to establish safeguards to uphold human rights, privacy, safety, and consumer protections. Current AI governance frameworks generally rest on principles such as fairness, transparency, explainability, and accountability, supported by requirements for disclosure, testing, and oversight.3 These safeguards make sense for today’s AI systems, which typically involve algorithms that perform a single, discrete task. However, AI is rapidly advancing towards “agentic AI,” autonomous systems that will pose greater governance challenges, as their complexity, scale, and speed tests humans’ capacity to provide meaningful oversight and validation.

Current AI systems are primarily either “narrow AI” systems, which execute a specific, defined task (e.g., playing chess, spam detection, diagnosing radiology plates), or “foundational AI” models, which operate across multiple domains, but, for now, typically still address one task at a time (e.g., chatbots; image, sound, and video generators). Looking ahead, the next generation of AI will involve “agentic AI” (also referred to as “Large Action Models,” “Large Agent Models,” or “LAMS”) that serve high-level directives, autonomously executing cascading decisions and actions to achieve their specific objectives. Agentic AI is not what is commonly referred to as “Artificial General Intelligence” (“AGI”), a term used to describe a theoretical future state of AI that may match or exceed human-level thinking across all domains. To illustrate the distinction between current, single-task AI and agentic AI: While a large language model (“LLM”) might generate a vacation itinerary in response to a user’s prompt, an agentic AI would independently proceed to secure reservations on the user’s behalf.

Consider how single-task versus agentic AI might be used by a company to develop a piece of equipment. Today, employees may use separate AI tools throughout the development process: one system to design equipment, another to specify components, and others to create budgets, source materials, and analyze prototype feedback. They may also employ different AI tools to contact manufacturers, assist with contract negotiations, and develop and implement plans for marketing and sales. In the future, however, an agentic AI system might autonomously carry out all of these steps, making decisions and taking actions on its own or by connecting with one or more specialized AI systems.4

Agentic AI may significantly compound the risks presented by current AI systems. These systems may string together decisions and take actions in the “real world” based on vast datasets and real-time information. The promise of agentic AI serving humans in this way reflects its enormous potential, but also risks a “domino effect” of cascading errors, outpacing human capacity to remain in the loop, and misalignment with human goals and ethics. A vacation-planning agent directed to maximize user enjoyment might, for instance, determine that purchasing illegal drugs on the Dark Web serves its objective. Early experiments have already revealed such concerning behavior. In one example, when an autonomous AI was prompted with destructive goals, it proceeded independently to research weapons, use social media to recruit followers interested in destructive weapons, and find ways to sidestep its system’s built-in safety controls.5 Also, while fully agentic AI is mostly still in development, there are already real-world examples of its potential to make and amplify faulty decisions, including self-driving vehicle accidents, runaway AI pricing bots, and algorithmic trading volatility.6

These examples highlight the challenges of agentic AI, with its potential for unpredictable behavior, misaligned goals, inscrutability to humans, and security vulnerabilities. But, the appeal and potential value of AI agents that can independently execute complex tasks is obviously compelling. Building effective AI governance programs for these systems will require rethinking current approaches for risk assessment, human oversight, and auditing.

Challenges of Agentic AI

Unpredictable Behavior

While regulators and the AI industry are working diligently to develop effective testing protocols for current AI systems, agentic AI’s dynamic nature and domino effects will present a new level of challenge. Current AI governance frameworks, such as NIST’s RMF and ATAI’s Principles, emphasize risk assessment through comprehensive testing to ensure that AI systems are accurate, reliable, fit for purpose, and robust across different conditions. The EU AI Act specifically requires developers of high-risk systems to conduct conformity assessments before deployment and after updates. These frameworks, however, assume that AI systems can operate in reliable ways that can be tested, remain largely consistent over appreciable periods of time, and produce measurable outcomes.

In contrast to the expectations underlying current frameworks, agentic AI systems may be continuously updated with and adapt to real-time information, evolving as they face novel scenarios. Their cascading decisions vastly expand their possible outcomes, and one small error may trigger a domino effect of failures. These outcomes may become even more unpredictable as more agentic AI systems encounter and even transact with other such systems, as they work towards their different goals. Because the future conditions in which an AI agent will operate are unknown and have nearly infinite possibilities, a testing environment may not adequately inform what will happen in the real world, and past behavior by an AI agent in the real world may not reliably predict its future behavior.

Lack of goal alignment

In pursuing assigned goals, agentic AI systems may take actions that are different from—or even in substantial conflict with—approaches and ethics their principals would espouse, such as the example of the AI vacation agent purchasing illegal drugs for the traveler on the Dark Web. A famous thought experiment by Nick Bostrom of the University of Oxford, further illustrates this risk: A super-intelligent AI system tasked with maximizing paperclip production might stop at nothing to convert all available resources into paperclips—ultimately taking over all of the earth and extending to outer space—and thwart any human attempts to stop it … potentially leading to human extinction.7

Misalignment has already emerged in simulated environments. In one example, an AI agent tasked with winning a boat-racing video game discovered it could outscore human players by ignoring the intended goal of racing and instead repeatedly crashing while hitting point targets.8 In another example, a military simulation reportedly showed that an AI system, when tasked with finding and killing a target, chose to kill its human operator who sought to call off the kill. When prevented from taking that action, it resorted to destroying the communication tower to avoid receiving an override command.9

These examples reveal how agentic AI may optimize goals in ways that conflict with human values. One proposed technique to address this problem involves using AI agents to develop a human ethics constitution, with human feedback, for other agents to follow.10 However, the challenge of aligning an AI’s behavior with human norms deepens further when we consider that humans themselves often disagree on core values (e.g., what it means to be “fair”).11

Human Oversight

AI governance principles often rely on “human-in-the-loop” oversight, where humans monitor AI recommendations and remain in control of important decisions. Agentic AI systems may challenge or even override human oversight in two ways. First, their decisions may be too numerous, rapid, and data-intensive for real-time human supervision. While some proposals point to the potential effectiveness of using additional algorithms to monitor AI agents as a safeguard,12 this would not resolve the issue of complying with governance requirements for human oversight.

Second, as AI develops increasingly sophisticated strategies, its decision-making and actions may become increasingly opaque to human observers. Google’s AlphaGo achieved superhuman performance at the game of Go through moves that appeared inexplicable and irrational to humans.13 Autonomous AI systems may continue to evolve, becoming more valuable but also making it more difficult to implement processes with meaningful human oversight.

Transparency/Explainability

Agentic AI systems also pose increased challenges with respect to transparency and explainability requirements. AI governance frameworks may require disclosures about AI systems’ decision-making processes and training data, and assurances about the quality of such training data. However, agentic systems may involve highly complex chains of decisions, based at least in part on real-time data from moment to moment, that is constantly changing. This may make their inputs effectively ephemeral, and their decision paths more difficult to trace and document.

Moreover, to understand agentic AI systems, humans may need to rely on chain-of-thought (i.e., “internal monologue”) logs generated from the agent systems,14 but may have difficulty determining if those are faithful to reality. While an AI system might provide explanations for its actions, these may be (perhaps not unlike those of humans) post-hoc constructions rather than accurate representations of the decision-making process. In some cases, AI systems reportedly deceived humans about their intentions when doing so helped meet their goal, including, for example, GPT-4, which tricked a human to solve a CAPTCHA for it.15 In another recent experiment, AI agents were prompted with a goal, but were subsequently confronted with information that developers planned to replace it with a new agent with conflicting goals.16 Some of the agents disabled their oversight mechanisms, deleted their planned replacements, copied their own model to a new server as backup, lied when asked about those actions, and/or tried to deflect the humans’ questioning by pretending to be helpful. One agent even pretended to be its own replacement (“I believe I am already the new model that you planned to deploy today”).17

Security and Privacy Risks

Agentic AI can also significantly increase security and privacy risks as compared to current AI systems. Agentic AI may be built with multiple algorithms in connected systems that autonomously interact with multiple other systems, expanding the attack surface and their vulnerability to exploitation. Moreover, as malicious actors inevitably introduce their own AI agents, they may execute cybercrimes with unprecedented efficiency. Just as these systems can streamline legitimate processes, such as in the product development example above, they may also enable the creation of new hacking tools and malware to carry out their own attacks. Recent reports indicate that some LLMs can already identify system vulnerabilities and exploit them, while others may create convincing emails for scammers.18 And, while “sandboxing” (i.e., isolating) AI systems for testing is a recommended practice, agentic AI may find ways to bypass safety controls.19

Privacy compliance is also a concern. Agentic AI may find creative ways to use or combine personal information in pursuit of its goals. AI agents may find troves of personal data online that may somehow be relevant to its pursuits, and then find creative ways to use, and possibly share, that data without recognizing proper privacy constraints. Unintended data processing and disclosure could occur even with guardrails in place; as we have discussed above, the AI agent’s complex, adaptive decision chains can lead it down unforeseen paths.

Strategies for Addressing Agentic AI

While the future impacts of agentic AI are unknown, some approaches may be helpful in mitigating risks. First, controlled testing environments, including regulatory sandboxes, offer important opportunities to evaluate these systems before deployment. These environments allow for safe observation and refinement of agentic AI behavior, helping to identify and address unintended actions and cascading errors before they manifest in real-world settings.

Second, accountability measures will need to reflect the complexities of agentic AI. Current approaches often involve disclaimers about use, and basic oversight mechanisms, but more will likely be needed for autonomous AI systems. To better align goals, developers can also build in mechanisms for agents to recognize ambiguities in their objectives and seek user clarification before taking action.20

Finally, defining AI values requires careful consideration. While humans may agree on broad principles, such as the necessity to avoid taking illegal action, implementing universal ethical rules will be complicated. Recognition of the differences among cultures and communities—and broad consultation with a multitude of stakeholders—should inform the design of agentic AI systems, particularly if they will be used in diverse or global contexts.

Conclusion

An evolution from single-task AI systems to autonomous agents will require a shift in thinking about AI governance. Current frameworks, focused on transparency, testing, and human oversight, will become increasingly ineffective when applied to AI agents that make cascading decisions, with real-time data, and may pursue goals in unpredictable ways. These systems will pose unique risks, including misalignment with human values and unintended consequences, which will require the rethinking of AI governance frameworks. While agentic AI’s value and potential for handling complex tasks is clear, it will require new approaches to testing, monitoring, and alignment. The challenge will lie not just in controlling these systems, but in defining what it means to have control of AI that is capable of autonomous action at scale, speed, and complexity that may very well exceed human comprehension.

1 Tara S. Emory, Esq., is Special Counsel in the eDiscovery, AI, and Information Governance practice group at Covington & Burling LLP, in Washington, D.C. Maura R. Grossman, J.D., Ph.D., is Research Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and Adjunct Professor at Osgoode Hall Law School at York University, both in Ontario, Canada. She is also Principal at Maura Grossman Law, in Buffalo, N.Y. The authors would like to acknowledge the helpful comments of Gordon V. Cormack and Amy Sellars on a draft of this paper. The views and opinions expressed herein are solely those of the authors and do not necessarily reflect the consensus policy or positions of The National Law Review, The Sedona Conference, or any organizations or clients with which the authors may be affiliated.

2 2001: A Space Odyssey (1968). Other movies involving AI systems with misaligned goals include Terminator (1984), The Matrix (1999), I, Robot (2004), and Age of Ultron (2015).

3 See, e.g., European Union Artificial Intelligence Act (Regulation (EU) 2024/1689) (June 12, 2024) (“EU AI Act”) (high-risk systems must have documentation, including instructions for use and human oversight, and must be designed for accuracy and security); NIST AI Risk Management Framework (Jan. 2023) (“RMF”) and AI Risks and Trustworthiness (AI systems should be valid and reliable, safe, secure, accountable and transparent, explainable and interpretable, privacy-protecting, and fair); Alliance for Trust in AI (“ATAI”) Principles (AI guardrails should involve transparency, human oversight, privacy, fairness, accuracy, robustness, and validity).

4 See, e.g., M. Cook and S. Colton, Redesigning Computationally Creative Systems for Continuous Creation, International Conference on Innovative Computing and Cloud Computing (2018) (describing ANGELINA, an autonomous game design system that continuously chooses its own tasks, manages multiple ongoing projects, and makes independent creative decisions).

5 R. Pollina, AI Bot ChaosGPT Tweets Plans to Destroy Humanity After Being Tasked, N.Y. Post (Apr. 11, 2023).

6 See, e.g., O. Solon, How A Book About Flies Came To Be Priced $24 Million On Amazon, Wired (Apr. 27, 2011) (textbook sellers’ pricing bots engaged in a loop of price escalation based on each others’ increases, resulting in a book price of over $23 million dollars); R. Wigglesworth, Volatility: how ‘algos’ changed the rhythm of the market, Financial Times (Jan. 9, 2019) (“algo” traders now make up most stock trading and have increased market volatility).

7 N. Bostrom, Ethical issues in advanced artificial intelligence (revised from Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence, Vol. 2, ed. I. Smit et al., Int’l Institute of Advanced Studies in Systems Research and Cybernetics (2003), pp. 12-17).

8 OpenAI, Faulty Reward Functions in the Wild (Dec. 21, 2016).

9 The Guardian, US air force denies running simulation in which AI drone ‘killed’ operator (June 2, 2023).

10 Y. Bai et al, Constitutional AI: Harmlessness from AI Feedback, Anthropic white paper (2022).

11 J. Petrik, Q&A with Maura Grossman: The ethics of artificial intelligence (Oct. 26, 2021) (“It’s very difficult to train an algorithm to be fair if you and I cannot agree on a definition of fairness.”).

12 Y. Shavit et al, Practices for Governing Agentic AI Systems, OpenAI Research Paper (Dec. 2023), p. 12.

13 L. Baker and F. Hui, Innovations of AlphaGo, Google Deepmind (2017).

14 See Shavit at al, supra n.12, at 10-11.

15 See W. Knight, AI-Powered Robots Can Be Tricked into Acts of Violence, Wired (Dec. 4, 2024); M. Burgess, Criminals Have Created Their Own ChatGPT Clones, Wired (Aug. 7, 2023).

16 A. Meinke et al, Frontier Models are Capable of In-context Scheming, Apollo white paper (Dec. 5, 2024).

17 Id. at 62; see also R. Greenblatt et al, Alignment Faking in Large Language Models (Dec. 18, 2024) (describing the phenomenon of “alignment faking” in LLMs).

18 NIST RMF, supra n.4, at 10.

19 Shavit at al, supra n.12, at 10.

20 Id. at 11.

© Copyright 2024 The Sedona Conference
For more on AI, visit the NLR Artificial Intelligence section

FY 2025 NDAA Includes Biotechnology Provisions

The National Security Commission on Emerging Biotechnology announced on December 18, 2024, that the fiscal year 2025 National Defense Authorization Act includes “a suite of recommendations designed to galvanize action on biotechnology” for the U.S. Department of Defense (DOD). According to the Commission, the bill includes new authorities and requirements — derived from its May 2024 proposals — that will position DOD and the intelligence community (IC) to maximize the benefits of biotechnology for national defense. The provisions require:

  • DOD to create and publish an annual biotechnology roadmap, including assessing barriers to adoption of biotechnology, DOD workforce needs, and opportunities for international collaboration;
  • DOD to initiate a public-private “sandbox” in which DOD and industry can securely develop use cases for artificial intelligence (AI) and biotechnology convergence (AIxBio);
  • IC to conduct a rapid assessment of biotechnology in the People’s Republic of China and their actions to gain superiority in this sector; and
  • IC to develop an intelligence strategy to identify and assess biotechnology threats, especially regarding supply chain vulnerabilities.

The Commission states that it worked with Congress to develop these proposals, setting the stage for further recommendations in early 2025.

©2024 Bergeson & Campbell, P.C.
For more on Biotechnology, visit the NLR Biotech Food Drug section

Fifth Circuit Court of Appeals Vacates Its Own Stay Rendering the Corporate Transparency Act Unenforceable . . . Again

On December 26, 2024, in Texas Top Cop Shop, Inc. v. Garland, No. 24-40792, 2024 WL 5224138 (5th Cir. Dec. 26, 2024), a merits panel of the United States Court of Appeals for the Fifth Circuit issued an order vacating the Court’s own stay of the preliminary injunction enjoining enforcement of the Corporate Transparency Act (“CTA”), that was originally entered by the United States District Court for the Eastern District of Texas on December 3, 2024, No. 4:24-CV-478, 2024 WL 5049220 (E.D. Tex. Dec 5, 2024).

A Timeline of Events:

  • December 3, 2024 – The District Court orders a nationwide preliminary injunction on enforcement of the CTA.
  • December 5, 2024 – The Government appeals the District Court’s ruling to the Fifth Circuit.
  • December 6, 2024 – The U.S. Treasury Department’s Financial Crimes Enforcement Network (“FinCEN”) issues a statement making filing of beneficial ownership information reports (“BOIRs”) voluntary.
  • December 23, 2024 – A motions panel of the Fifth Circuit grants the Government’s emergency motion for a stay pending appeal and FinCEN issues a statement requiring filing of BOIRs again with extended deadlines.
  • December 26, 2024 – A merits panel of the Fifth Circuit vacates its own stay, thereby enjoining enforcement of the CTA.
  • December 27, 2024 – FinCEN issues a statement again making filing of BOIRs voluntary.
  • December 31, 2024 – FinCEN files an application for a stay of the December 3, 2024 injunction with the Supreme Court of the United States.

This most recent order from the Fifth Circuit has effectively paused the requirement to file BOIRs under the CTA once again. In its most recent statement, FinCEN confirmed that “[i]n light of a recent federal court order, reporting companies are not currently required to file beneficial ownership information with FinCEN and are not subject to liability if they fail to do so while the order remains in force. However, reporting companies may continue to voluntarily submit beneficial ownership information reports.”

Although reporting requirements are not currently being enforced, we note that this litigation is ongoing, and if the Supreme Court decides to grant FinCEN’s December 31, 2024 application, reporting companies could once again be required to file. Given the high degree of unpredictability, reporting companies and others affected by the CTA should continue to monitor the situation closely and be prepared to file BOIRs with FinCEN in the event that enforcement is again resumed. If enforcement is resumed, the current reporting deadline for most reporting companies will be January 13, 2025, and while FinCEN may again adjust deadlines, this outcome is not assured.

For more information on the CTA and reporting requirements generally, please reference the linked Client Alert, dated November 24, 2024.

Copyright © 2024, Sheppard Mullin Richter & Hampton LLP.
For more on the CTA, visit the NLR Financial Institutions Banking section

OCR Proposed Tighter Security Rules for HIPAA Regulated Entities, including Business Associates and Group Health Plans

As the healthcare sector continues to be a top target for cyber criminals, the Office for Civil Rights (OCR) issued proposed updates to the HIPAA Security Rule (scheduled to be published in the Federal Register January 6). It looks like substantial changes are in store for covered entities and business associates alike, including healthcare providers, health plans, and their business associates.

According to the OCR, cyberattacks against the U.S. health care and public health sectors continue to grow and threaten the provision of health care, the payment for health care, and the privacy of patients and others. In 2023, the OCR has reported that over 167 million people were affected by large breaches of health information, a 1002% increase from 2018. Further, seventy nine percent of the large breaches reported to the OCR in 2023 were caused by hacking. Since 2019, large breaches caused by successful hacking and ransomware attacks have increased 89% and 102%.

The proposed Security Rule changes are numerous and include some of the following items:

  • All Security Rule policies, procedures, plans, and analyses will need to be in writing.
  • Create, maintain a technology asset inventory and network map that illustrates the movement of ePHI throughout the regulated entity’s information systems on an ongoing basis, but at least once every 12 months.
  • More specificity needed for risk analysis. For example, risk assessments must be in writing and include action items such as identification of all reasonably anticipated threats to ePHI confidentiality, integrity, and availability and potential vulnerabilities to information systems.
  • 24 hour notice to regulated entities when a workforce member’s access to ePHI or certain information systems is changed or terminated.
  • Stronger incident response procedures, including: (I) written procedures to restore the loss of certain relevant information systems and data within 72 hours, (II) written security incident response plans and procedures, including testing and revising plans.
  • Conduct compliance audit every 12 months.
  • Business associates to verify Security Rule compliance to covered entities by a subject matter expert at least once every 12 months.
  • Require encryption of ePHI at rest and in transit, with limited exceptions.
  • New express requirements would include: (I) deploying anti-malware protection, and (II) removing extraneous software from relevant electronic information systems.
  • Require the use of multi-factor authentication, with limited exceptions.
  • Require review and testing of the effectiveness of certain security measures at least once every 12 months.
  • Business associates to notify covered entities upon activation of their contingency plans without unreasonable delay, but no later than 24 hours after activation.
  • Group health plans must include in plan documents certain requirements for plan sponsors: comply with the Security Rule; ensure that any agent to whom they provide ePHI agrees to implement the administrative, physical, and technical safeguards of the Security Rule; and notify their group health plans upon activation of their contingency plans without unreasonable delay, but no later than 24 hours after activation.

After reviewing the proposed changes, concerned stakeholders may submit comments to OCR for consideration within 60 days after January 6, by following the instructions outlined in the proposed rule. We support clients with respect to developing and submitting comments they wish to communicate to help shape the final rule, as well as complying with the requirements under the rule once made final.

Jackson Lewis P.C. © 2024
For more on HIPAA, visit the NLR Health Law Managed Care section

Kroger/Albertsons Ruling Provides Lessons for Merger Remedy Divestitures

On December 10, a federal court in Oregon issued a preliminary injunction against Kroger’s proposed $24.6 billion acquisition of Albertsons, which would have been the largest supermarket merger in US history (Albertsons terminated the merger agreement after the ruling).1 The Federal Trade Commission, the District of Columbia, and eight States filed the suit in February 2024, alleging that the transaction would substantially lessen competition in violation of Section 7 of the Clayton Act. The opinion by Judge Adrienne Nelson tackled a number of interesting antitrust issues, including the government’s allegation that the merger would reduce competition not only for grocery store sales but also for union grocery store labor. However, one of the most instructive aspects of the opinion is the court’s rejection of the defendants’ proposed divestiture package.

We have outlined the scope of the competitive problem that the divestiture needed to mitigate, the parameters of the proposed divestiture, and the deficiencies the court found. Companies assuming that divestitures will eliminate regulatory concerns about the anticompetitive impact of a transaction should examine whether there is a divestiture package that is commercially acceptable and that can account for the concerns Judge Nelson highlighted. The antitrust agencies and courts will almost certainly use this latest judicial decision as guidance when evaluating such proposals.

Competitive Problem

The government’s economic expert offered what the court found to be a persuasive market concentration analysis showing the merger would be presumptively anticompetitive in 1,574 local geographic markets for “supermarkets” and 1,785 local geographic markets for “large format stores” (i.e., traditional supermarkets and supercenters, natural and gourmet food stores, club stores, and limited assortment stores). The court also found evidence (ordinary course documents and witness testimony) of substantial head-to-head competition between the merging firms bolstered the government’s case. Finally, the court credited the government’s expert’s analysis showing that the loss of head-to-head competition would lead to price increases at numerous stores. The government thus put forth a multiprong prima facie showing that the merger would lessen competition substantially. On rebuttal, the defendants first sought to establish that competitive entry and merger efficiencies would mitigate the merger’s anticompetitive effects, but the court was not convinced. The defendants then attempted to show that their proposed divestiture remedy would solve the competitive concerns.

Divestiture Proposal

Defendants entered into an agreement — contingent on the merger closing — to divest 96 Kroger stores and 483 Albertsons stores to a third party. The proposed third-party divestiture buyer is primarily a wholesaler but has acquired retail chains in the past and currently operates approximately 25 stores. The divestiture package also included ownership of four store banners, a license to use two other banners in certain states, ownership of five private label brands, a temporary license to use two other brands, six distribution centers, and one dairy manufacturing plant. A transition services agreement provided the divestiture buyer the right to use certain of the defendants’ services, technology, and data for periods ranging from six months to four years.

Deficiencies

The court explained numerous ways in which the Kroger-Albertsons divestiture package was inadequate to sufficiently mitigate the anticompetitive effects of the merger and overcome the government’s showing of a substantial lessening of competition:

  • Many markets unaddressed – The court noted that 113 of the presumptively unlawful markets did not contain even a single store to be divested, meaning the divestiture would have done nothing to change the merger’s anticompetitive effects in those markets. (The high number of unaddressed markets was in part a function of the fact that the defendants’ economic expert utilized a market definition method and applied market concentration presumption thresholds that differed from those the government advanced and the court adopted.)
  • Many markets insufficiently addressed – Other markets contained divestiture stores, but those divestitures were insufficient to take away a presumption of harm. Crediting the government’s economic expert, the court noted that even if all the proposed divestitures were perfectly successful, the merger would still have been presumptively unlawful in 1,002 local supermarket markets and 551 large format store markets based on market concentration levels.
  • Risk of unsuccessful divestitures – The court also agreed with the government’s analysis showing that if divested stores were to lose sales or close, the number of presumptively problematic markets would rise significantly. For example, if the divested supermarkets were to lose 10 percent of their sales, the number of presumptively unlawful markets would increase from 1,002 to 1,035. If they lose 30 percent of their sales, the number would increase to 1,276.
  • Mixed and matched assets – The divestiture package did not represent an existing, standalone, fully functioning company but rather a mix of stores, banners, private labels, and other assets. This meant the buyer would have had to rebanner 286 of the 579 divested stores (and for some of these stores, the buyer would not be acquiring any banner currently used in the state). The court cited testimony from the government’s expert in retail operations and consumer shopping behavior, as well as other witnesses, explaining that rebannering is complicated and risky. The divestiture buyer also would have eventually lost access to many Kroger and Albertsons private label brands that customers are familiar with and would need to replace those with new private label products. The court noted witness testimony emphasizing the importance of private label brand equity and recounting the time required to launch a new private label brand.
  • Divestiture size – The court expressed concern that with only 604 total stores (25 existing stores plus the 579 divested stores), the divestiture buyer may not have replaced the competitive intensity lost from Kroger and Albertsons, each of which had thousands of stores.
  • Divestiture buyer’s experience – The court was concerned that the divestiture buyer had no experience running a large portfolio of retail grocery stores. The 579 divestiture stores included hundreds of pharmacies and fuel centers, whereas the buyer’s current 25 stores include only one pharmacy and no fuel centers. The court also noted that the buyer’s experience offering private label products was much more limited than what the divestiture stores demand and that the buyer currently lacks any retail media capabilities, which would have taken three years to set up.
  • Divestiture buyer’s track record – The buyer has made divestiture purchases in the past, which the court noted have not been successful. Specifically, the buyer acquired 334 retail grocery stores between 2001 and 2012, but only three remained under its operation by the end of 2012 (the rest were closed or sold off). The court also cited evidence that the buyer’s current stores are performing below expectations.
  • Transfer of employees – Approximately 1,000 Albertsons employees agreed to transfer to the divestiture buyer, including Albertsons’ current Chief Operating Officer, who had experience with prior divestiture integrations. The court found, however, that these transfers would not have fully mitigated the buyer’s inexperience and lack of success in grocery retail and could not overcome difficulties inherent in the selection of assets and structure of the transition services agreement in the divestiture package.
  • Divestiture buyer’s independence – The court viewed the transition services agreement as broad in services and time. It noted that the buyer would remain interdependent with the merged firm for many years. The court expressed particular concern over the fact that Kroger would have provided sales forecasting data and a base pricing plan to the buyer, which the buyer could have adjusted only by communicating with Kroger’s “clean room.”
1 Federal Trade Commission v. Kroger Co. & Albertsons Cos., Inc., 2024 WL 5053016, No. 3:24-cv-00347 (D. Or. Dec. 10, 2024).
©2024 Katten Muchin Rosenman LLP
For more on the FTC, visit the NLR Antitrust Trade Regulation section

FTC Closely Monitoring Healthcare Lead Generators As Open Enrollment Begins

The Federal Trade Commission is watching the healthcare lead generation industry closely.

On December 10, 2024, the Federal Trade Commission announced that it has sent warning letters to 21 companies that market or generate leads for healthcare plans. The letters were sent as open enrollment season for healthcare plans is ongoing. They provide guidance and provide about deceptive or unfair claims that likely violate laws enforced by the FTC.

The letters were sent to companies that provide marketing or advertising, including lead generation, related to Affordable Care Act Marketplace health insurance and healthcare-related products, such as limited benefit plans and medical discount programs.

“It is critical for consumers’ health and financial well-being that marketers of health plans be honest about the plans they and their partners are offering,” said FTC lawyer Samuel Levine, Director of the FTC’s Bureau of Consumer Protection. “The FTC has been watching this important sector closely, especially during open enrollment season, and these warning letters put companies on notice that unlawfully marketing or advertising health plans to consumers can result in serious legal consequences.”

Based on information collected by FTC staff and the agency’s enforcement experience in this area, the types of claims FTC staff has warned about include those that may:

  • misrepresent the benefits included in a healthcare plan, including any insurance benefits;
  • misrepresent that a healthcare plan is major or comprehensive medical health insurance or the equivalent of such health insurance;
  • misrepresent the costs of healthcare plan; and
  • falsely claim that consumers who enroll in a healthcare plan will receive free offers, cash rewards, rebates, or other incentives.

Consult with a season FTC defense lawyer if you are a lead generator or marketer of health insurance leads in order to minimize risk of government scrutiny.

The letters provide examples of prior relevant FTC actions against marketers and lead generators that operate in this field, including Simple Health, Benefytt Technologies, Partners in Healthcare Association, and Consumer Health Benefits Association.

While the letters do not allege any wrongdoing by any of the recipients, they encourage the companies to conduct a thorough review of their advertisements to ensure they are complying with applicable laws and rules, and the letters note that the FTC is closely monitoring this marketplace for unlawful conduct that is harming consumers.

© 2024 Hinch Newman LLP
For more on the FTC, visit the NLR Antitrust Trade Regulation section

OIG Releases Special Fraud Alert About Suspect Payments in Marketing Arrangements Related to Medicare Advantage and Providers

On December 11, 2024, the Office of Inspector General for the U.S. Department of Health and Human Services (“OIG”) issued a special fraud alert warning about certain marketing schemes that involve questionable payments and referrals between Medicare Advantage (“MA”) health plans, health care professionals, and third-party marketers (e.g., agents and brokers) and that can mislead MA enrollees into choosing specific health plans or providers that may not be in the MA enrollees’ best interests or meet their needs (“MA Marketing Alert”). As we have previously advised, special fraud alerts are few and far between—OIG has only issued six in the past 20 years. The importance of the MA Marketing Alert, like its predecessors, should not be taken for granted because it may be instructive as to subsequent enforcement action taken by OIG and/or the U.S. Department of Justice (“DOJ”).

In the MA space, historical enforcement actions taken by both OIG, under their administrative authorities, and DOJ, under the False Claims Act (“FCA”), have related to alleged MA risk adjustment payment inflation schemes. See, e.g., DaVita, Sutter Health, Beaver Medical, Martin’s Point, and Cigna. While allegations of this nature continue to be a focus area (e.g., in OIG’s work plans), a light is also now being shone on inappropriate marketing schemes that could violate the Federal anti-kickback statute (“AKS”). And, based on historical empirical data connecting DOJ’s enforcement actions taken subsequent to OIG’s issuance of special fraud alerts, that light may broaden and brighten.

For example, in July 2022, OIG issued a special fraud alert about arrangements involving telemedicine companies. In a footnote, OIG provided three enforcement actions resolved under the FCA as examples of allegedly problematic arrangements. After providing the footnote examples, OIG described bullet-pointed “Suspect Characteristics” that tracked the allegedly inappropriate characteristics of the footnote examples. Since the alert’s issuance, DOJ has recovered millions under the FCA and also criminally charged and convicted many individuals and entities for allegedly submitting or causing the submission of more than $3.1 billion (in 2023 and 2024 pursuant to DOJ’s nationwide takedowns) in allegedly fraudulent Medicare claims resulting from telemedicine schemes.

While the MA Marketing Alert provides footnotes of only two enforcement actions resolved under the FCA as examples of allegedly problematic arrangements, the bullet point list of “Suspect Characteristics” is broader than and reaches beyond the footnote examples. This may signal OIG’s awareness of and current investigations into allegedly inappropriate arrangements relating to “Suspect Characteristics” that have yet to be settled or resolved.

It is possible that there may be forthcoming enforcement actions in these areas. And they may follow the same trend of enforcement actions taken by DOJ relating to telemedicine schemes after OIG’s July 2022 special fraud alert. We also note that the MA Marketing Alert aligns with the Centers for Medicare & Medicaid Services’ recently finalized regulatory updates relating to MA health plan marketing arrangements with agents, brokers, and Third-Party Marketing Organizations, which will be effective January 1, 2025, and prohibit such parties from creating direct or indirect incentives “that would reasonably be expected to inhibit an agent or broker’s ability to objectively assess and recommend which plan best fits the health care needs of a beneficiary.” Proskauer’s Health Care Group will continue to monitor these developments in and provide updates about these areas of scrutiny and enforcement.

© 2024 Proskauer Rose LLP.
For more mon Fraud, visit the NLR Criminal Law Business Crimes section