Legal Considerations When Implementing AI

May 24, 2024

As generative artificial intelligence, specifically large language models (referred to here generally as “AI”), continue to impact various aspects of business operations, companies are increasingly looking to integrate AI into daily workflows. However, while AI offers numerous prospective benefits to companies and their employees, such as enhanced efficiency, improved decision-making, and cost savings, it also introduces a variety of legal concerns. These concerns range from data privacy and intellectual property rights to regulatory compliance and liability issues. In this overview, we explore some of the key legal concerns for companies when utilizing AI, and what companies can do along with legal counsel to mitigate the associated risks.

I. Confidentiality

AI systems often utilize proprietary algorithms and data, making the protection of trade secrets and confidential information a priority. This is particularly true when working with third-party language models and other vendors where the company is not in absolute and complete control of the data.

Companies must implement stringent security measures to prevent unauthorized access or leaks and ensure that employees understand their obligations regarding the handling of sensitive information. Companies must also educate employees to never include confidential information or trade secrets as part of AI input data, particularly on public AI systems, and implement processes of compliance, legal, and technical review and approval prior to the use of any potentially sensitive information.

To comply with confidentiality requirements while interacting with public AI systems, companies can implement the following best practices:

• Data Anonymization: Ensure that any sensitive or personal information is anonymized before inputting it into an AI chatbot. Remove or mask identifiable details to protect individual privacy.
• Data Use Disclosure: Receive consent from clients or customers to use certain data in connection with AI systems, which should be part of a company’s thorough and comprehensive privacy and data usage policies.
• Data Minimization: Only share the minimum amount of information necessary with the AI chatbot. Avoid inputting unnecessary sensitive details that do not contribute to the chatbot’s functionality.
• Monitoring and Logging: Implement robust monitoring and logging mechanisms to track interactions with the AI chatbot. This helps in detecting and investigating any unauthorized access or potential breaches promptly.
• Compliance With Regulations: Ensure that AI chatbot interactions comply with relevant data protection and privacy regulations, such as GDPR, HIPAA, or CCPA. Regularly review and update practices to remain aligned with evolving legal requirements.

II. Intellectual Property

The use of AI in generating new content, inventions, or designs raises complex intellectual property (IP) issues. Determining ownership of inputs used in connection with AI systems as well as AI-generated outputs is important, especially when employees use company resources to create them. Clear policies defining IP ownership and rights to AI-generated works and innovations are essential to avoid legal disputes.

Input Data

Clarifying ownership and usage rights for input data fed into AI systems is critical to avoid disputes over data ownership and control. This includes understanding who owns the data, how it can be used, and the rights of employees and third parties involved.

Employers should establish clear agreements with employees regarding the ownership of data utilized during employment. These agreements should specify the rights and responsibilities of both parties concerning data access, transfer, and deletion upon termination of employment.

When using third-party data as part of AI input, companies must ensure they have the necessary licenses and permissions to use the data. This includes understanding any restrictions on data usage and ensuring compliance with data sharing agreements.

Output Data

Companies must establish policies governing the ownership, use, licensing, and protection of proprietary AI-generated insights, analyses, and predictions. Companies should also ensure commercial or external uses of AI system outputs are properly reviewed by legal counsel to avoid potential infringement of third-party IP rights.

One of the key challenges relating to output data is that current IP laws are not fully equipped to handle AI-generated works, and while intellectual property offices such as the U.S. Copyright Office and U.S. Patent and Trademark Office have provided some guidance, ownership and protection of AI-generated works remains an area of uncertainty. Companies must establish clear guidelines on ownership, including policies to ensure clear and detailed documentation of the portions of works that are generated by AI and the portions utilizing human innovation and creativity. Additionally, companies should consider incorporating clauses in employment contracts that address IP rights related to AI-generated content.

IP Protections 

Safeguarding AI innovations through IP protection is essential for maintaining a competitive edge and preventing unauthorized use or reproduction by competitors. Companies should consider securing patents, copyrights, or trade secrets to protect AI algorithms, models, and applications developed in-house.

• Patents: Filing for patents on novel AI techniques and algorithms can help protect innovative solutions and provide a competitive advantage. This includes conducting thorough patent searches to ensure the novelty and non-obviousness of AI innovations.
• Copyrights: Securing copyrights for software and data compilations can help protect the creative aspects of AI innovations. This includes ensuring that AI-generated works meet the criteria for copyright protection, such as originality and fixation.
• Trade Secrets: Protecting AI innovations as trade secrets can provide a competitive advantage and prevent unauthorized use by competitors. This involves implementing measures to keep proprietary information confidential, such as NDAs, restricted access, and employee training on confidentiality.

III. Contractual Obligations 

When outsourcing AI development or utilizing third-party AI services, companies must carefully review and negotiate contractual agreements to clarify rights, responsibilities, and liabilities related to data usage, IP ownership, and compliance with legal and regulatory requirements.

Due Diligence (Know Your Vendor)

Conducting due diligence on AI vendors can help assess their reputation, expertise, and compliance with industry standards and best practices. This includes evaluating the vendor’s security practices, data handling procedures, and record of compliance.

Contracts with AI vendors should address key legal considerations, such as data security, confidentiality, indemnification, and limitations of liability. This includes specifying the rights and responsibilities of both parties, outlining data usage and ownership rights, and establishing processes for data breach notification and response.

Companies should also establish clear service-level agreements with AI vendors to help ensure services are delivered as expected and that performance standards are met. This includes specifying metrics for service quality, availability, and response times, as well as outlining remedies for non-compliance.

Quality Control (Avoid Overreliance)

Using AI to draft or review contracts without subsequent review by a trained human poses significant risks. When using an AI to review a contract, the AI, while advanced, may lack the nuanced understanding of legal principles, context-specific subtleties, and the ability to interpret complex human intentions. Without the quality control provided by human reviewers, companies may commit to unfavorable or otherwise problematic provisions, which will be binding on the company.

AI is prone to “hallucinations” which occur when an AI, such as a natural language processing model, generates information or responses that appear plausible but are actually incorrect, unfounded, or nonsensical. These hallucinations stem from the model’s reliance on patterns and probabilities derived from its training data, rather than an understanding of factual accuracy or real-world context. This tendency is particularly problematic in the legal realm, where there can be real world consequences of the digital hallucination.

So, while AI review can become an effective and powerful tool for teams that often interact with legal documents, including sales, human resources, and others, it is critical to have policies and safeguards in place to account for AI’s shortcomings. Otherwise, companies may find themselves unwinding their savings when led astray by AI.

IV. Data Privacy 

Data privacy is a critical concern for companies leveraging AI technologies. AI systems often rely on large datasets to train algorithms and make predictions, which may include sensitive personal information. Ensuring compliance with data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the ever-expanding set of state privacy laws in the United States, is essential to avoid legal penalties and protect individual privacy rights.

Companies must implement robust data protection measures to safeguard personal data. This includes obtaining explicit consent from individuals for data to be used by AI systems, minimizing data collection to what is necessary for specific purposes, and ensuring data is processed lawfully and transparently. Companies must also provide mechanisms for individuals to exercise their rights with respect to data, such as the right to access, rectify, or delete their data.

Additionally, companies should review their current data security processes and ensure these are updated, as needed, in order to help prevent data breaches and unauthorized access. Companies should also establish clear protocols for responding to data breaches, including notifying affected individuals and relevant authorities.

V. Bias in AI Systems

Addressing bias and promoting fairness in AI systems is both a moral and, in some cases, legal obligation. Companies should implement measures to identify, mitigate, and monitor biases in AI algorithms to minimize the risk of discrimination claims and regulatory scrutiny.

For instance, in hiring processes, AI algorithms might favor certain demographic groups over others if the training data reflects historical biases. This can lead to violations of equal employment opportunity laws. Implementing bias detection tools can help identify and mitigate biases in AI algorithms.

Additionally, conducting regular fairness audits can help ensure that AI systems operate fairly and equitably. This includes assessing the impact of AI-driven decisions on different demographic groups and making necessary adjustments to algorithms and processes.

VI. Conclusion

The integration of AI into the workplace presents a wide range of legal concerns for companies. By proactively addressing these concerns and implementing appropriate policies and processes, companies can navigate the ever-evolving landscape of AI integration effectively while harnessing the transformative potential of AI to drive innovation and growth.