Is your M&A target a manufacturing company with automated production, a consumer products business with online sales and marketing, or an education company creating content for students? Artificial intelligence (“AI”) systems and products, especially The increasing use and development of generative AI is creating risks for businesses that use such tools. AI plays a role in many industries and businesses where the product or service itself is not AI. In the context of M&A transactions, it is important to identify these risks and allocate responsibility. Risks of AI include infringement (including use of training data and output), confidentiality, ownership and protection of intellectual property (including limitations on protection of intellectual property generated by AI), regulation (privacy, recent AI-related legislation) and other risks arising from its use, such as indemnity obligations and the control of contractors' use of AI.
Even companies that are not focused on AI, such as when AI may be used in the business but not in the target product, can identify AI risks through due diligence and final appropriate conditions. It is important to properly address and allocate these risks. Acquisition agreement. From healthcare to manufacturing to entertainment, companies in many industries are using or interacting with AI. Fortunately, many AI-related risks can be partially covered by commonly used IP representations and warranties. This article examines the representations and warranties in purchase agreements that may include AI-related risks and issues and provides some comments on how these terms can be leveraged to address AI-specific risks. To do.
In some cases, existing IP representations and warranties may be extended to treat the AI technology definition as its own defined term or incorporated into other IP-related definitions such as “software” or “technology” as appropriate. can also do. AI technologies may include generative AI tools such as ChatGPT or AI-inclusive tools such as MSWord with Co-Pilot integration, and the definition should consider the specific use by the target. These types of tools use machine learning algorithms and large amounts of training data to develop models that can generate output in the form of high-quality text, images, and other content based on user input. If these tools are used by the target, it may also be necessary to incorporate the concept of “training data” to eliminate his AI risk for the target business. Training data is typically described as data, fine-tuning, and RAGS consent used to train, pre-train, validate, or evaluate, improve, modify, or supplement software algorithms or models. In general, the actual scope of the definition will depend on the results of the evaluation of the company in question. For example, diligence may also reveal whether the target has established policies for employees regarding the use of AI in the business. Reviewing these policies could impact these definitions as well as when the target uses contractors to create work products using her AI.
As part of the diligence carried out, the purchaser should, among other things, check its rights regarding the use of the tools and ownership of the output. In such cases, you may be liable for (i) infringement and rights in your training data; (ii) ownership of the AI technology; (iii) ownership and protectability of any output developed using the AI technology tools or software; and (iv) ) a license for input from the AI Technology, and (v) if applicable, separate copyright and patent representations and warranties related to the AI Technology. The actual scope of the representations and warranties will vary depending on what AI technologies and tools a company uses and the purpose of the product, such as customer-facing or internal purposes. In M&A transactions where the target company uses AI, it is important to understand the risks of AI and create appropriate clauses to appropriately allocate and mitigate these risks. To draft appropriate terms, parties can consider a combination of (a) due diligence, (b) disclosure statements, (c) standard existing representations, or (d) custom representations.
