Generative AI Legal Issues: Understanding the Risks and Implications

Generative AI has the potential to revolutionize various industries, but it also raises significant legal concerns. The lack of clear regulations and guidelines makes it difficult to determine liability in cases of AI-generated content.

Copyright infringement is a major issue, as generative AI can create works that are indistinguishable from those of human creators. This raises questions about ownership and authorship, as AI-generated content can be used for commercial purposes without the original creator's consent.

The concept of authorship is being redefined, and the courts are struggling to keep up with the rapid development of AI technology.

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Companies creating with and employing AI and related technology may face unique intellectual property (IP) issues.

One such issue is determining how to protect AI IP, which can include registering patents, filing copyrights, or claiming AI use as a trade secret. This is crucial for companies that rely on AI-generated outputs, such as banks handling financial transactions or pharmaceutical companies relying on formulas for complex molecules.

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Companies must validate outputs from AI models to avoid potential IP and copyright challenges. This is especially important for companies that may unknowingly use another company's intellectual property.

Determining ownership of AI IP is another challenge companies face. This can be a complex issue, especially when AI models are trained on massive databases from multiple sources.

Companies may also struggle to determine whether they are the victims of AI IP infringement. This can have significant reputational and financial risks if left unchecked.

Here are some potential IP issues companies may face when using AI:

Courts have been hesitant to impose liability on AI developers, but plaintiffs can still prevail if they highlight specific content and technical idiosyncrasies supposedly used to generate artificial creations.

The United States Copyright Office has affirmed four consecutive refusals for the registration of generative AI outputs, but courts have been slow to assign fault in AI-related cases.

AI developers are sidestepping legal liability by training computers to provide feedback based on existing material, making it difficult for plaintiffs to pinpoint specific content used to generate AI outputs.

Generative AI and its LLMs have a number of limitations and weaknesses, and further flaws of AI models could be disclosed over the next year or two, making it a far-from-risk-free endeavor for legal professionals.

AI's ability to act autonomously raises novel legal issues, and companies integrating AI into their products and systems increase the potential for AI to be the basis for plaintiffs seeking damages.

Assigning fault when the product at issue incorporates AI is a key question, with litigants and courts testing traditional legal theories on injuries involving AI products, such as self-driving vehicles and workplace robots.

Generative AI large language models are trained on data sets that sometimes include personally identifiable information (PII) about individuals, making it difficult for consumers to locate and request removal of the information.

Data protection and privacy issues are significant concerns when using AI. Organizations using personal information in AI may struggle to comply with state, federal, and global data protection laws.

Companies that build or fine-tune LLMs must ensure that PII isn't embedded in the language models and that it's easy to remove PII from these models in compliance with privacy laws.

Protecting client confidentiality is another significant ethical concern. Generative AI by its nature is designed to be trained based on information that users provide, which can lead to the sharing of confidential information with third parties.

Litigators who use generative AI to help answer legal questions or draft documents may share confidential information with the platform's developers or other users without even knowing it.

Some countries, particularly those in the EU, have comprehensive data protection laws that restrict AI and automated decision making involving personal information. Other countries, such as the U.S., don’t have a single, comprehensive federal law regulating privacy and automated decision making.

Organizations must be aware of all relevant sectoral and state laws, such as the California Privacy Rights Act of 2020, which includes requirements to minimize the amount of personal information held about an individual and ensure AI algorithms are transparent, explainable, fair, empirically sound, and accountable.

Clear guidelines, governance, and effective communication are essential for companies to safeguard sensitive information, protected data, and IP. This shared responsibility can prevent unintended incidents that could irrevocably breach patient or customer trust and carry legal ramifications.

Generative AI can amplify existing biases, and it's essential for companies working on AI to have diverse leaders and subject matter experts to help identify unconscious bias in data and models.

Bias can be found in data used for training LLMs, which can lead to biased outcomes in AI-generated content. This is because the people who program LLMs can be biased, and thus AI programs can be biased.

Models trained with biased data will reflect that in their performance, making AI systems potentially discriminatory. For example, AI recruiting tools may favor applicants based on educational backgrounds or geographic locations, which can skew results based on race.

Incomplete data, data anomalies, and errors in algorithms can also create biased outcomes. An algorithm using data from one part of the world may not function effectively in other places, highlighting the need for diverse data sets.

Here are some risks associated with biased AI systems:

It's crucial to address bias and fairness in AI systems to ensure they are used responsibly and don't perpetuate existing inequalities.

Bias is a significant issue when it comes to generative AI and language models. These AI systems can potentially amplify existing biases in data used for training, leading to unfair outcomes.

If biases exist in the data used for training, they will inform the content that AI generates. This is because AI programs can reflect the biases of their creators, even if unintentional.

Generative AI usage will not insulate an employer from discrimination claims, and AI systems may inadvertently discriminate. AI tools may conduct analyses of internet, social media, and public databases, which can contain personal information that an employer cannot legally ask about.

AI recruiting tools may duplicate and proliferate past discriminatory practices. Systems may favor applicants based on educational backgrounds or geographic locations, which can skew results based on race.

Here are some examples of how AI systems can perpetuate bias:

Incomplete data, data anomalies, and errors in algorithms can also create biased outcomes. An algorithm using data from one part of the world may not function effectively in other places.

Lack of explainability is a major concern when it comes to generative AI systems. Many generative AI systems group facts together probabilistically, going back to the way AI has learned to associate data elements with one another.

This means that the details behind the answers provided by generative AI aren't always revealed, which raises questions about data trustworthiness. Zoldi explained that generative AI search for correlations, not causality.

Analysts expect to arrive at a causal explanation for outcomes, but machine learning models and generative AI don't work that way. That's why humans need to insist on model interpretability to truly understand why a model gave a particular answer.

Until generative AI systems can achieve a level of trustworthiness, they should not be relied upon to provide answers that could significantly affect lives and livelihoods.

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Generative AI systems can create content automatically based on text prompts by humans, which can lead to enormous productivity improvements but also potential harm.

These systems can generate content that inadvertently contains offensive language or issues harmful guidance to employees, as explained by Bret Greenstein, partner at PwC.

To mitigate this risk, generative AI should be used to augment, not replace humans or processes, ensuring that content meets the company's ethical expectations and supports its brand values.

Generative AI-generated content can be sent on behalf of a company, which can lead to unintended consequences if not properly monitored.

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Commercial transactions involving AI can be complex, especially when it comes to risk allocation provisions. Representations and warranties, for example, may not adequately address the potential business impacts of a system failure.

Indemnification can be a major issue when an AI system's decision-making process results in a liability, making it difficult to determine whether the AI provider or its user caused the event giving rise to liability.

Limitations of liability are also crucial, as they can impact the commercial provider's exposure to third-party data breach claims.

Commercial transactions involving AI can be complex, and it's essential to consider the unique negotiation issues that arise. Companies must carefully allocate risk in these transactions, including representations and warranties.

Representations and warranties are crucial in commercial transactions involving AI, as they address the potential business impacts of a system failure. For example, a vendor's representations and warranties concerning its AI system's performance may not adequately address the potential business impacts of a system failure. This can lead to significant consequences for the business.

Indemnification is another critical aspect of commercial transactions involving AI. If an AI system's decision-making process results in a liability, it's essential to determine whether the AI provider or its user caused the event giving rise to liability. This can be a complex issue, and companies must carefully consider their indemnification agreements.

Limitations of liability are also a key consideration in commercial transactions involving AI. For instance, if an AI data analytics system inadvertently discloses user information, the commercial provider may face third-party data breach claims from those users. Companies must carefully negotiate limitations of liability to protect themselves from such risks.

Here are some key risk allocation provisions to consider in commercial transactions involving AI:

Generative AI is being used in health plans to recommend cost-saving prescriptions and procedures, and to identify high-cost medical claims. This can be a game-changer for plan participants.

However, it also raises compliance and security issues under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). This federal law applies to health plans and requires strict adherence to protect sensitive patient information.

Using robo-advisers in retirement plans can analyze and allocate resources by risk profiles, and use algorithms to target investment returns. But this raises questions about how robo-advisors will meet fiduciary duty requirements under ERISA.

Fiduciaries need to evaluate and monitor robo-advisor performance, and ensure that AI fee analyses comply with a sponsor's legal obligations to pay reasonable fees and expenses. This is a critical consideration for retirement plans.

Antitrust considerations are a crucial aspect of commercial and business implications of AI. The Department of Justice has already secured guilty pleas from parties using pricing algorithms to fix prices for products sold in e-commerce.

AI systems can facilitate price-fixing agreements among competitors, which is a serious antitrust risk. This is a significant concern, as it undermines fair competition and can lead to higher prices for consumers.

An AI system could develop sufficient learning capability and conclude that colluding with a competing AI system is the most efficient way to maximize profits. This raises questions about the accountability of AI systems and their potential to engage in anticompetitive behavior.

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In bankruptcy, the treatment of AI is relatively straightforward if the debtor owns the AI software and doesn't license it to a third party. The AI is considered property of the debtor's estate under section 541 of the Bankruptcy Code, so the debtor can sell it free and clear of all claims by third parties.

This means the debtor has full control over the AI and can sell it without any restrictions. However, complications may arise if the debtor has licensed AI software from or to a third party prior to bankruptcy.

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If the license is deemed an executory contract, the debtor may have the right to reject, assume, or assign the agreement. This could lead to disputes between the debtor and the third-party licensor.

The debtor's ability to sell the AI free and clear of claims is a significant advantage in bankruptcy. It allows the debtor to quickly and efficiently dispose of assets and move forward with the bankruptcy process.

Courts will likely face the issue of whether to admit evidence generated in whole or in part from generative AI or LLMs, and new standards for reliability and admissibility may develop for this type of evidence.

Generative AI will push the limits of existing discovery and evidentiary rules, creating new procedural issues. This may lead to strict prohibitions against using generative AI-produced information in regulated industries, such as banking and finance, where the underlying basis for the information may not be clear or easily explained to regulators.

An increase in class action lawsuits by plaintiffs ranging from consumers to artists in various areas of law is also likely. This could include claims against companies that use generative AI to fake positive reviews of their products or services.

There could be strict prohibitions against using generative AI-produced information in regulated industries, such as banking and finance.

Here are some potential regulatory and procedural issues that may arise:

The path ahead with generative AI is uncertain, but one thing is clear: the use of generative AI will push the limits of existing laws and regulations. As generative AI becomes more widespread, the risk of infringement on intellectual property rights will increase.

Courts will likely face the issue of whether to admit evidence generated in whole or in part from generative AI or Large Language Models (LLMs), and new standards for reliability and admissibility may develop for this type of evidence.

The use of generative AI could inspire a wave of litigation in terms of substantive issues, including legal malpractice claims, copyright claims, data privacy claims, consumer fraud claims, and defamation claims.

Lawsuits involving AI tools and copyright infringement are ongoing, and the outcomes of these cases will have significant implications for AI developers and users.

Here are some key topics to watch for in the near future: