Artificial Intelligence Key Legal Issues: A Comprehensive Overview for Businesses and Legal Professionals

Artificial intelligence is no longer a subject for futurists and computer scientists. It is embedded in the operating infrastructure of modern commerce—powering supply chains, screening job applicants, setting prices, reviewing contracts, diagnosing diseases, and driving cars. The technology has moved from the research laboratory to the boardroom with astonishing speed, and the law is racing to keep up.

For businesses deploying AI and for the lawyers advising them, the legal landscape is both vast and volatile. AI implicates virtually every major area of commercial law: intellectual property, products liability, data privacy, employment discrimination, antitrust, commercial contracting, bankruptcy, and an increasingly dense web of federal, state, and international regulation. What makes AI uniquely challenging from a legal perspective is not the novelty of any single issue—liability, ownership, and privacy are ancient concerns—but the way AI collapses these issues into a single technology that acts, learns, and produces outputs in ways that existing legal frameworks were never designed to address.

This guide provides a comprehensive overview of the key legal issues arising from the commercial development, deployment, and licensing of AI systems. It is designed for business executives, in-house counsel, and outside practitioners who need a working understanding of the legal risks and strategic considerations that attend virtually every aspect of AI in commerce. For issues specific to AI-generated intellectual property or generative AI copyright disputes, we encourage readers to consult our dedicated analyses of those topics.

What AI Is—And Why It Defies Easy Legal Categories

Before examining specific legal issues, it is worth pausing on a threshold question that pervades every area of AI law: what, exactly, is artificial intelligence? The answer matters because legal rules are built on categories—product or service, author or tool, employee or contractor—and AI has an uncomfortable habit of straddling the boundaries between them.

There is no single, universally accepted legal definition of AI. The term generally refers to computer systems capable of performing tasks that would ordinarily require human intelligence: recognizing patterns in data, drawing inferences, making predictions, learning from experience, and generating outputs—text, images, code, decisions—that can be indistinguishable from human work product. The EU AI Act (Regulation (EU) 2024/1689), the world's first comprehensive AI statute, defines an AI system as "a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments."

In practice, AI encompasses a broad range of technologies: natural language processing (the technology behind legal research platforms and chatbots), machine learning (algorithms that improve their performance over time by learning from data), artificial neural networks (the architecture underlying image recognition and generative AI), and robotic systems that combine AI with physical actuation. These technologies are deployed across industries in applications ranging from contract review and e-discovery to autonomous vehicles, algorithmic trading, and medical diagnostics.

The legal significance of this definitional ambiguity cannot be overstated. Whether an AI-enabled product is classified as a "product" or a "service" determines whether strict liability applies. Whether an AI system is considered an "author" or an "inventor" determines whether its outputs receive intellectual property protection. Whether an AI hiring tool constitutes an "employment test" determines what anti-discrimination obligations attach. At every turn, the legal analysis of AI begins with a classification question that existing categories were not built to answer.

Intellectual Property: Protecting AI and What AI Creates

The intersection of artificial intelligence and intellectual property law presents two distinct sets of questions. The first concerns the protection of AI itself—the algorithms, source code, training data, and models that constitute an AI system. The second concerns the ownership and protectability of what AI produces—the inventions, creative works, and data sets generated by AI systems operating with varying degrees of autonomy. Both sets of questions are central to the practice of IP law in the age of generative AI.

Patent Protection for AI Technology

Certain forms of AI technology are patentable. The United States Patent and Trademark Office (USPTO) expressly recognizes AI through its designation of Class 706 (Data Processing: Artificial Intelligence) in the patent classification system and devotes two examination units to reviewing AI-related applications. The foundational question, however, is whether a given AI invention clears the threshold of patent-eligible subject matter under Section 101 of the Patent Act (35 U.S.C. § 101).

The challenge is well known. The Supreme Court held in Diamond v. Chakrabarty, 447 U.S. 303 (1980), that patent protection extends to "anything under the sun that is made by man," but excluded abstract ideas, laws of nature, and natural phenomena from the scope of patentable subject matter. In Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208 (2014), the Court established a two-step framework for evaluating patent eligibility: first, determine whether the claims are directed to an abstract idea; and second, if so, search for an "inventive concept"—an element or combination of elements sufficient to transform the claim into something "significantly more" than the abstract idea itself.

Because AI technology is fundamentally based on algorithms and mathematical computations—categories that the USPTO has identified as potentially abstract ideas (see USPTO Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019))—patent applicants must draft their claims carefully to survive Section 101 scrutiny. Effective strategies include characterizing the invention in terms of its practical application (such as improving computer functionality or implementing the algorithm in conjunction with specific hardware), including claim elements that are not well-understood, routine, or conventional, and describing the invention in terms of structure rather than pure functionality to avoid rejections under 35 U.S.C. § 112.

Even where patenting is available, it carries significant trade-offs. The patent application process takes years—an eternity in the fast-moving AI sector. It requires public disclosure of the claimed invention, potentially revealing valuable trade secrets. And a patent's default term of 20 years from filing (35 U.S.C. § 154(a)(2)) may be shorter than the protection available through other IP mechanisms.

Copyright Protection for AI Software

Copyright protection is available for certain components of AI systems, most notably source code. An original expression of source code is protectable as a "literary work" under the Copyright Act if it is original and fixed in a tangible medium of expression (17 U.S.C. § 102(a); Sega Enters. v. Accolade, Inc., 977 F.2d 1510, 1520 (9th Cir. 1992)). Visual elements of AI programs—graphical user interfaces, screen displays—may also qualify for protection.

However, copyright has significant limitations in the AI context. Protection extends only to the original expression embodied in the code, not to the functional aspects of the software: algorithms, logic, system design, and formatting are excluded. Proof of infringement requires evidence of actual copying—a more demanding standard than patent infringement. And the fair use defense (17 U.S.C. § 107) can shield conduct that might otherwise constitute infringement, a doctrine with particular relevance to AI training data, as discussed below. Additionally, AI software that incorporates open source components may present ownership and enforcement complications that require careful licensing analysis.

Trade Secret Protection for AI

For many AI developers, trade secret protection is the most important and practical form of IP coverage. Trade secrets are protected at the federal level under the Defend Trade Secrets Act of 2016 (DTSA) (18 U.S.C. §§ 1831–1839) and at the state level under versions of the Uniform Trade Secrets Act (UTSA) adopted by every state except New York. Trade secret protection applies broadly to business, financial, and technical information—including source code, algorithms, model architectures, and training data sets—that is not generally known, derives independent economic value from its secrecy, and is the subject of reasonable efforts to maintain that secrecy.

The advantages of trade secret protection for AI are substantial. Trade secrets require no application or registration process, no public disclosure, and can last indefinitely so long as the information remains secret and the owner continues to take reasonable protective measures. For companies whose competitive advantage lies in proprietary algorithms or curated training data, trade secrets may be the most valuable IP asset in the portfolio.

The challenge lies in the "reasonable efforts" requirement. AI technology evolves rapidly through iterative development, creating a continuous obligation to identify and protect new trade secrets as they emerge. Companies should implement comprehensive protection programs including physical and digital access controls, multi-factor authentication, data loss prevention measures, non-disclosure agreements, written policies governing employee access, and, in an era of widespread remote work, robust protocols for securing trade secrets outside the traditional office environment.

| IP Form | Protectable AI Elements | Key Advantages | Key Limitations | |---|---|---|---| | Patent | Novel AI methods, systems, hardware | Strongest exclusionary rights; protects functionality; no need to prove copying | Section 101 eligibility hurdles; requires public disclosure; 20-year term; multi-year prosecution | | Copyright | Source code, object code, visual elements | Automatic upon creation; long duration (life + 70 years for individual authors); no registration required for protection | Protects expression only, not function; fair use defense; requires proof of copying | | Trade Secret | Algorithms, training data, model weights, architectures | No registration; no public disclosure; potentially indefinite duration | Requires continuous reasonable efforts; lost if information becomes public; no protection against independent discovery or reverse engineering |

The Ownership Problem: Who Owns What the Machine Creates?

The most provocative IP question in AI law is not how to protect AI, but who owns what AI produces. When an AI system generates an invention, writes code, composes music, or produces a visual work, does the output belong to anyone—and if so, to whom?

AI Inventorship and Patent Ownership. The Federal Circuit resolved the threshold patent question in Thaler v. Vidal, 43 F.4th 1207 (Fed. Cir. 2022), holding that the Patent Act requires an inventor to be an "individual"—a term the Supreme Court has explained "ordinarily means a human being." Mohamad v. Palestinian Auth., 566 U.S. 449, 454 (2012). Stephen Thaler had filed patent applications naming his AI system DABUS (Device for the Autonomous Bootstrapping of Unified Science) as the sole inventor. The USPTO, the Eastern District of Virginia, and the Federal Circuit all agreed: AI cannot be an inventor under current law. The Supreme Court denied certiorari in April 2023, making the Federal Circuit's holding settled law throughout the United States. Courts in the United Kingdom, Australia, and New Zealand have reached the same conclusion. Only South Africa—whose patent system does not require substantive examination—has granted a DABUS patent.

Critically, the Thaler court noted that it was "not confronted today with the question of whether inventions made by human beings with the assistance of AI are eligible for patent protection." That question—the far more commercially relevant one—has been addressed by the USPTO through evolving administrative guidance. In 2024, then-Director Kathi Vidal issued guidance applying the joint inventorship framework from Pannu v. Iolab Corp., 155 F.3d 1344 (Fed. Cir. 1998), allowing patents where the human inventor made a "more than insignificant contribution" to the AI-assisted invention. In November 2025, new Director John Squires revised this approach, treating AI as merely a "tool" (analogous to laboratory equipment) and establishing a presumption of human inventorship so long as a natural person is named as the inventor—a shift that significantly relaxes the practical barriers to patenting AI-assisted inventions. For a deeper analysis, see our article on AI-generated inventions and ownership.

AI Authorship and Copyright Ownership. The copyright question tracks a parallel path. The Copyright Act does not define "author," but courts and the U.S. Copyright Office have consistently required human authorship. In the "monkey selfie" case, the Ninth Circuit held that a monkey had no rights to photographs the monkey took of himself. Naruto v. Slater, 888 F.3d 418 (9th Cir. 2018). The Copyright Office has applied the same principle to AI-generated works, refusing registration for images created by AI systems without sufficient human creative input. The D.C. Circuit reached a similar result in 2025 in another case brought by Stephen Thaler, holding that creative works must have a human author to be copyrightable. These decisions leave open the question of how much human involvement is required when AI is used as a tool in the creative process—a question with enormous commercial implications for industries from music licensing to digital content creation.

AI and IP Infringement

AI creates infringement risk on multiple fronts. On the patent side, divided infringement issues arise when multiple actors use different steps of a patented AI method or system, and liability questions multiply when an AI system autonomously generates code that may infringe third-party patents. Companies should consider obtaining freedom-to-operate opinions before launching AI products and including robust indemnification provisions in AI license agreements.

On the copyright side, the use of copyrighted works to train AI systems has become the central legal battleground of the generative AI era. Plaintiffs including The New York Times, Getty Images, and groups of visual artists have filed high-profile copyright infringement suits against AI developers, alleging that the ingestion of copyrighted training data constitutes unauthorized reproduction. Defendants argue that the training process is protected by fair use, relying in part on the Second Circuit's holding in The Authors Guild, Inc. v. Google, Inc., 804 F.3d 202 (2d Cir. 2015), that Google's unauthorized digitization of books for search purposes was transformative and therefore fair. These cases are still working their way through the courts, and their outcome will shape the economics of AI development for a generation.

Products Liability: When AI Causes Harm

As companies integrate AI into physical products and decision-making systems, the potential for AI to cause injury, property damage, and economic loss grows. AI's capacity for autonomous action raises a foundational question that existing products liability law was not designed to answer: how do you assign fault when the "actor" is a machine?

Applying Traditional Liability Theories to AI

Products liability law in the United States rests on three traditional theories: negligence, breach of warranty, and strict liability under Section 402A of the Restatement (Second) of Torts. Each theory can be applied to AI-related injuries, but each presents unique complications.

Negligence requires proof that the defendant failed to exercise reasonable care. In Cruz v. Raymond Talmadge d/b/a Calvary Coach, 2015 WL 13776213 (Mass. Suffolk Cty. Super. Ct. Sept. 25, 2015), plaintiffs injured when a bus struck an overpass brought negligence claims against GPS device manufacturers, alleging that the devices defectively directed the driver under an overpass too low for the vehicle and failed to warn of the danger. The case applied traditional design defect and failure-to-warn theories to a semi-autonomous AI device whose outputs could be traced back to identifiable design choices made by specific companies.

Strict liability under Section 402A imposes liability without fault on sellers of products in a "defective condition unreasonably dangerous" to the user. This theory applies straightforwardly when a physical product incorporating AI malfunctions. But what about an AI system whose "defect" lies not in faulty manufacturing but in a training data set that produces biased or incorrect outputs? Courts have not yet resolved whether data-driven errors constitute "defects" within the meaning of traditional products liability doctrine.

The autonomous actor problem becomes most acute with fully autonomous AI systems. In Nilsson v. General Motors, LLC, No. 18-471 (N.D. Cal. 2018), a motorcyclist claimed injury when an autonomous vehicle veered into his lane. The plaintiff alleged that the vehicle itself "drove in a negligent manner"—and GM admitted in its answer that "the Bolt was required to use reasonable care in driving." Although the case settled before reaching substantive rulings, it raised questions that remain unresolved: Can an AI product itself be the "actor" for liability purposes? What standard of care applies—the "reasonable person" standard, or a new "reasonable machine" standard? And when fault cannot be traced to a human decision, should courts apply res ipsa loquitur, shifting the burden to manufacturers? At least one commentator has argued that this approach would be appropriate. See David C. Vladeck, Machines Without Principles: Liability Rules and Artificial Intelligence, 89 Wash. L. Rev. 117 (2014).

| Liability Theory | Application to AI | Key Challenge | |---|---|---| | Negligence | Design defect; failure to warn; negligent training data curation | Identifying the specific human decision that constitutes the breach of care | | Breach of Warranty | Express warranty on AI performance; implied warranty of merchantability | Determining who in the development chain bears warranty liability—the AI developer, the integrator, or the product manufacturer | | Strict Liability (§ 402A) | Defective AI-enabled product causing physical harm | Defining "defect" in systems that learn and evolve after leaving the manufacturer's control |

Insurance Considerations

The insurance industry is still adapting to AI-related risks. Traditional commercial general liability (CGL) policies, cyber insurance, errors and omissions (E&O) coverage, and product liability insurance may or may not cover a given AI failure, depending on its nature and the policy language. All parties in the AI supply chain—developers, integrators, and deployers—should review existing coverage, identify gaps, and evaluate whether supplemental or specialized AI liability insurance is necessary.

Data Privacy and AI: Navigating a Global Patchwork

AI systems are data-hungry by design. Their performance improves with access to larger and more diverse data sets—a technical imperative that collides directly with data protection laws designed to minimize the collection and use of personal information. The tension between AI's data appetite and privacy law's data-minimization principle is one of the defining legal challenges of the AI era.

The Fairness Problem

Many data protection laws require organizations to process personal information "fairly." This principle demands transparency, non-discrimination, and respect for individuals' reasonable expectations. AI challenges each of these requirements. Machine learning algorithms may incorporate the biases of their human creators or the historical patterns in their training data, producing discriminatory outcomes that are difficult to detect and even harder to explain. Incomplete data sets, data anomalies, and algorithmic errors can compound these problems.

The EU's General Data Protection Regulation (Regulation (EU) 2016/679) (GDPR) specifically addresses fairness in automated decision-making. The GDPR defines "profiling" as any form of automated processing of personal data used to analyze or predict an individual's performance, economic situation, health, preferences, interests, reliability, behavior, location, or movements (Article 4(4)). Articles 13 and 14 require organizations to inform individuals of the existence of automated decision-making and to provide "meaningful information about the logic involved" and "the significance and the envisaged consequences" of the processing—requirements that may be difficult to satisfy when the AI model functions as a "black box" whose internal reasoning is opaque even to its developers.

In the United States, the Federal Trade Commission has taken an increasingly active enforcement posture toward AI. The FTC's April 2020 guidance stressed that AI algorithms should be transparent, explainable, fair, empirically sound, and designed to foster accountability. The agency's "Operation AI Comply" enforcement initiative targets deceptive AI practices, and the FTC has signaled that it will use its existing authority under Section 5 of the FTC Act to challenge AI systems that produce discriminatory outcomes or mislead consumers.

Purpose Limitation and Data Minimization

Two additional data protection principles—purpose limitation and data minimization—pose structural challenges for AI development. Purpose limitation requires that personal information be collected only for specified, explicit, and legitimate purposes and not processed in a manner incompatible with those purposes. But machine learning algorithms may discover unexpected correlations that suggest entirely new uses for the data—uses that were not contemplated when the data was collected. An algorithm trained to assess creditworthiness might discover correlations between lifestyle factors and credit risk that, while statistically significant, were never disclosed to the data subjects as a purpose of collection.

Data minimization requires organizations to collect no more personal information than is necessary for the stated processing purpose. AI, by contrast, thrives on maximum data. The more data an AI system can access, the more sophisticated its pattern recognition and the more accurate its predictions. Organizations must navigate this tension by establishing in advance the scope of data necessary for the algorithm, de-identifying data sets to the extent possible through pseudonymization or encryption, and implementing robust information governance and retention schedules.

The Emerging Regulatory Landscape

The regulatory framework for AI is evolving rapidly and diverging significantly across jurisdictions.

The EU AI Act (Regulation (EU) 2024/1689), which entered into force on August 1, 2024, is the world's first comprehensive AI statute. It establishes a risk-based framework, classifying AI systems into prohibited, high-risk, and lower-risk categories with corresponding regulatory obligations. The Act's implementation is phased: prohibited AI practices and AI literacy obligations became effective February 2, 2025; obligations for general-purpose AI (GPAI) models applied from August 2, 2025; most remaining transparency obligations apply from August 2, 2026; and rules for high-risk AI systems in regulated products apply from August 2, 2027. Violations can result in fines ranging from €7.5 million to €35 million, or 1.5% to 7% of global turnover, depending on the severity and the entity's size.

In the United States, there is no single federal AI statute. Instead, the regulatory landscape consists of a growing patchwork of state laws—with California, Colorado, New York, and Illinois adopting or proposing comprehensive AI and algorithmic accountability legislation—complemented by federal agency enforcement actions, particularly from the FTC. On December 11, 2025, President Trump issued an executive order seeking to centralize AI policy at the federal level, directing the DOJ to identify and challenge state AI laws deemed overly burdensome, and pressing Congress to enact a uniform national framework. The executive order signals a potential shift toward federal preemption of state AI regulation, though the legal and political path forward remains uncertain.

Sector-specific regulations also apply. The Fair Credit Reporting Act (FCRA) regulates automated credit decisions. The Illinois Artificial Intelligence Video Interview Act (Ill. HB 2557) governs AI in hiring. The California Privacy Rights Act (Cal. Civ. Code §§ 1798.100–1798.199.100) directs the attorney general to issue regulations on consumers' rights regarding automated decision-making. And HIPAA governs the use of AI in healthcare contexts where protected health information is involved.

AI in the Workplace: Discrimination, Safety, and Displacement

Hiring Algorithms and Anti-Discrimination Law

One of the fastest-growing applications of AI is in employee recruiting, screening, and hiring. AI promises to streamline these processes by automatically sorting, ranking, and eliminating candidates with minimal human oversight. But the same technology that promises to reduce human bias can also amplify it—at scale and at speed.

Employers using AI hiring tools must comply with federal, state, and local anti-discrimination laws prohibiting both intentional discrimination (disparate treatment) and facially neutral practices that disproportionately affect protected classes (disparate impact). Under Title VII of the Civil Rights Act (42 U.S.C. § 2000e-2(k)(1)(A)), once a plaintiff demonstrates that an employment practice has a disproportionate adverse effect on a protected class, the employer must show that the practice is "job related for the position in question and consistent with business necessity"—and that no less discriminatory alternative exists.

AI hiring tools present distinctive challenges under both theories of liability. For disparate treatment, the algorithmic "black box"—the opacity of the decision-making process—cuts both ways. On one hand, the lack of transparency may make it difficult for plaintiffs to prove intentional discrimination through direct evidence. On the other hand, under the McDonnell Douglas burden-shifting framework that governs most discrimination claims, the black box may make it equally difficult for employers to articulate the "legitimate, nondiscriminatory reasons" behind AI-driven adverse decisions.

For disparate impact, the risks are more acute. AI algorithms analyzing large data sets may identify statistical correlations between applicant characteristics and predicted job performance that have no actual causal relationship—and that disproportionately screen out members of protected classes. Moreover, when an employer uses the same algorithm across its entire candidate pool, plaintiffs may find it easier to establish the "common practice" element required for class certification, potentially converting individual discrimination claims into class actions with devastating exposure.

The Americans with Disabilities Act (ADA) adds another layer of risk. AI tools that analyze speech patterns in recorded video interviews may negatively evaluate individuals with speech impediments or hearing impairments who are otherwise qualified for the position. Employers must monitor AI tools for discriminatory impact on individuals with disabilities and ensure that online recruitment platforms are accessible to hearing- and sight-impaired applicants.

Workplace Safety and AI Robotics

The deployment of AI-powered robots alongside human workers introduces workplace safety concerns under the Occupational Safety and Health Act (OSH Act) and its general duty clause. OSHA's existing guidance on robotic safety was developed for traditional industrial robots and does not specifically address "intelligent" AI robots that work alongside employees, adapt their behavior over time, and operate with varying degrees of autonomy. When a workplace accident involving an AI robot occurs, the employer may face the "black box problem" in a different context: an inability to explain why the robot did what it did, and therefore an inability to satisfy OSHA or other regulators that adequate steps have been taken to prevent recurrence.

Employees injured by AI robots may also have avenues for recovery beyond workers' compensation. While workers' compensation is typically an employee's exclusive remedy for workplace injuries, it does not bar tort claims against third-party manufacturers or suppliers of the robotic equipment—a potential for expanded liability that product manufacturers must factor into their risk management strategies.

Workforce Displacement

When AI automates tasks previously performed by human workers, resulting layoffs must comply with the Worker Adjustment and Retraining Notification (WARN) Act and applicable state equivalents. Employers must also ensure that automation-driven layoffs do not disparately impact protected classes—a particular concern for older workers under the Age Discrimination in Employment Act (ADEA), who may be disproportionately affected when AI replaces functions traditionally performed by experienced employees. In unionized workplaces, the decision to implement AI and its effects on bargaining unit employees may constitute mandatory subjects of collective bargaining.

Commercial Transactions: Licensing, Risk Allocation, and Data Rights

Organizations acquiring AI systems from third parties face unique transactional issues that require careful attention from information technology and commercial counsel.

Representations, Warranties, and Indemnification

AI license agreements must address the vendor's representations and warranties about the system's performance, accuracy, and compliance with applicable law. Because customers typically integrate AI into mission-critical functions—automating production lines, making marketing decisions, screening employees—the consequences of system failure can be catastrophic. The non-infringement warranty deserves particular scrutiny: an AI system may independently produce infringing code or outputs during operation, and the allocation of liability for such infringement is far from settled. Indemnification provisions must clearly allocate responsibility for AI-driven liabilities between the developer and the deployer, a task complicated by the difficulty of determining which party "caused" an AI system's autonomous decision. Limitation of liability caps should be calibrated to the potential scale of harm—which, for an AI system controlling a production line or processing personal data, may be orders of magnitude greater than the contract's fee structure.

Data Rights and Aggregation

AI-based services agreements frequently include provisions allowing the vendor to aggregate and anonymize customer data to improve the AI system for all users. Customers benefit from a larger data universe but are understandably reluctant to authorize the use of their proprietary data to benefit competitors. The negotiation of data ownership, aggregation, anonymization, and confidentiality terms is therefore a critical element of any AI services agreement. Where the data includes personal information—purchasing histories, health records, financial data—the parties must also comply with applicable privacy laws, including obtaining necessary consents and implementing data protection measures that satisfy the requirements of the GDPR, the CCPA, HIPAA, and other applicable regimes.

AI and Antitrust: The Algorithmic Collusion Problem

AI pricing algorithms can assimilate vast quantities of competitive intelligence and adjust prices in real time—capabilities that create significant value but also significant antitrust risk. The risk manifests in two forms.

Facilitated collusion occurs when competitors use AI tools to implement or enforce a traditional price-fixing agreement. The Department of Justice has already secured guilty pleas in cases involving the use of pricing algorithms to fix prices in e-commerce. In one prosecution, online sellers of posters agreed to fix prices and used a common pricing algorithm to coordinate their pricing changes. This is straightforward antitrust liability—the AI is merely a tool for implementing an agreement that is illegal per se under Section 1 of the Sherman Act.

Autonomous collusion presents a harder question. As AI systems become more sophisticated, they may independently converge on supracompetitive pricing strategies without any human communication or agreement. An AI system might learn that the profit-maximizing strategy is to avoid aggressive price competition, effectively reaching a tacit understanding with competing algorithms. Whether this constitutes an "agreement" under the antitrust laws—or merely lawful unilateral conduct—is a question that antitrust enforcers worldwide are actively debating. The European Commission has suggested that companies should be held responsible for the anticompetitive actions of their AI systems and should build compliance measures into algorithmic design from the outset. U.S. law, which generally requires evidence of an agreement to establish a Section 1 violation, may need to evolve to address algorithmic coordination that defies traditional notions of conspiracy.

To minimize antitrust exposure, companies deploying pricing algorithms should maintain detailed records of the AI's design objectives, monitor the algorithm's outputs for anticompetitive patterns, and consider whether competitors are using the same or similar AI systems in the same market.

AI in Bankruptcy: Protecting IP Assets

The treatment of AI technology in bankruptcy follows established principles of intellectual property law, with some important nuances. AI systems are typically protected by a combination of patents, copyrights, and trade secrets—all of which qualify as "intellectual property" under Section 101(35A) of the Bankruptcy Code (11 U.S.C. § 101(35A)). When a debtor owns AI software outright, it is property of the estate under Section 541 and may be sold free and clear of claims.

Complications arise when the debtor has licensed AI to third parties. Section 365(n) provides special protections for non-debtor IP licensees, allowing them to retain their rights under the license even if the debtor rejects the executory contract. If the debtor is the licensor of a patent or non-exclusive copyright, courts have generally held that the debtor cannot sell the IP free and clear of the rights retained by licensees. See In re Sunbeam Prods. v. Chi. Am. Mfg., LLC, 686 F.3d 372, 377–78 (7th Cir. 2012). AI licensees should be aware of these protections—and AI developers should structure their licensing relationships with bankruptcy scenarios in mind.

Healthcare AI: HIPAA Compliance and Fiduciary Obligations

AI applications in healthcare—from diagnostic tools to wellness apps to robo-advisors for retirement plans—implicate a distinct set of regulatory obligations.

Under HIPAA, AI application developers may qualify as "business associates" of covered entities if they create, receive, maintain, or transmit protected health information on behalf of a health plan or provider. HHS guidance from 2016 provides a framework for analyzing this question based on whether the AI app operates independently on behalf of the consumer or on behalf of a covered entity. Where the developer is a business associate, it must comply with HIPAA's privacy and security requirements and enter into a Business Associate Agreement—obligations enforced through an increasingly aggressive HHS enforcement program.

In the retirement plan context, AI robo-advisors raise fiduciary duty issues under ERISA. Plan sponsors must evaluate whether robo-advisors are providing investment "education" (which is not subject to ERISA fiduciary standards) or investment "advice" (which is), and must monitor the performance and fees of AI-based service providers with the same diligence they would apply to human advisors. The alignment of AI decision-making with ERISA's prudent expert standard and duty of loyalty remains an evolving area of law.

Practical Compliance Checklist for AI Deployment

Intellectual Property

Conduct a comprehensive IP audit of AI assets, identifying which elements are best protected by patent, copyright, or trade secret. Implement robust trade secret protection programs for proprietary algorithms and training data. Obtain freedom-to-operate opinions before launching AI products. Ensure AI development processes include human inventive contributions sufficient to support patent applications. Review all training data for potential copyright infringement exposure.

Data Privacy and Regulatory Compliance

Map all personal data flows through AI systems. Assess compliance with the GDPR, EU AI Act, applicable U.S. state privacy laws, and sector-specific regulations. Implement algorithmic fairness testing and bias auditing. Prepare meaningful transparency disclosures about automated decision-making. Establish data minimization and retention protocols.

Products Liability and Risk Management

Review insurance coverage for AI-specific risks. Negotiate clear liability allocation in AI procurement and licensing agreements. Implement post-deployment monitoring for AI system performance and safety. Document design decisions and safety testing for all AI-enabled products.

Employment and Workplace

Audit AI hiring and screening tools for disparate impact. Ensure ADA accessibility of AI recruitment platforms. Monitor AI-driven workplace safety systems and document compliance with OSHA obligations. Negotiate automation and technology clauses in collective bargaining agreements.

Antitrust

Document the design objectives and competitive rationale of pricing algorithms. Monitor algorithmic outputs for patterns suggesting anticompetitive coordination. Assess whether competitors are using the same AI tools in the same markets.

Conclusion: Preparing for a Rapidly Evolving Landscape

Artificial intelligence is not a single legal issue—it is a force multiplier that touches every area of commercial law simultaneously. The companies that will navigate this landscape successfully are those that integrate legal analysis into AI strategy from the earliest stages of development, rather than treating legal compliance as an afterthought. This means assembling cross-functional teams that include IP counsel, privacy lawyers, employment specialists, commercial transactional attorneys, and technical advisors who can translate between legal requirements and engineering realities.

The law governing AI is not static. The EU AI Act is still being phased in. U.S. federal policy is shifting toward a national framework. State legislatures are introducing AI-related bills by the hundreds. Courts are deciding cases of first impression on AI inventorship, authorship, training data, and liability. Practitioners who stay current with these developments—and who understand not just the law as it is but the direction in which it is moving—will be best positioned to advise clients through what promises to be one of the most dynamic periods in the history of commercial regulation.

Our intellectual property and technology practice works at the intersection of these issues, advising companies on the full spectrum of AI legal risk—from patent prosecution and trade secret protection to IP litigation, commercial licensing, and regulatory compliance. For companies developing, deploying, or acquiring AI systems, the time to engage experienced counsel is before the first line of code is written.

This article is for informational purposes only and does not constitute legal advice. For guidance on specific AI-related legal issues, please consult qualified counsel.