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As a part of our ongoing series on artificial intelligence (AI), Kenneth Adler, chair of Loeb & Loeb’s Technology & Sourcing practice, discusses AI’s impact on technology and sourcing. Ken examines how businesses opting to adopt AI technology must modify contracts to address the emerging risks related to AI, such as data usage and management, intellectual property, compliance and liability.
AI is ubiquitous, addressing the universal desire for efficiency across various tasks. While there appear to be many benefits of using AI, the risks associated with its technology, such as bias and data collection, have yet to be regulated. With AI seemingly integrated everywhere, what proactive legal steps can businesses take to address potential risks and protect themselves? As the landscape evolves, is it possible to future-proof contracts from the risks of AI? In this Q&A, Ken explores these topics, discusses what’s top of mind for clients and shares his outlook for the next three to five years in the space.
Tell us about your practice and the type of matters you generally handle.
In my practice, I handle complex global and domestic technology and outsourcing transactions for clients spanning various industries, including health care, retail, technology, finance and insurance. This includes all aspects of drafting and negotiating all types of technology and outsourcing agreements. I also regularly support clients with addressing the creation of strategies related to AI (generative AI, machine learning, deep learning) and other automation technologies, cloud computing, multi-sourced environments, as well as renegotiation and termination of existing outsourcing and IT-related agreements. Additionally, my practice extends to a variety of data security, privacy and intellectual property matters.
With advancements in AI impacting nearly every industry, what emerging trends in technology and sourcing are you currently observing?
Companies are aware that the use of AI is inevitable and is already underway. In the health care sector, AI plays a pivotal role in diagnosis, monitoring and treatment. In the retail space, AI is increasingly being used for logistics purposes. Finally, the insurance industry leverages AI to streamline tasks such as establishing premiums and claims adjudication. The shift in the use of AI has clients particularly focused on managing data assets and understanding where and how their data is being accessed and used with AI solutions.
In crafting contracts for AI technologies, how can agreements be customized to reflect industry-specific challenges and opportunities, ensuring that businesses are legally compliant and strategically positioned in their tech and sourcing endeavors?
The implementation of AI poses commercial and legal challenges that need to be addressed in AI contracts. These contracts should specify details related to the applicable industry, transaction, type of AI involved and data usage, as it is not a one-size-fits-all approach. The agreement needs to address the respective rights of the parties concerning ownership and use of AI, including AI developments and enhancements. Additionally, it should be tailored for the data involved and permissible uses as the data is processed by or interacts with the AI technology.
As AI systems become more autonomous, how can businesses establish clear lines of liability and accountability?
Until applicable law catches up with the risks of AI technology, agreements pertaining to AI usage must proactively allocate responsibility for the associated risks, including compliance and liability for AI use. While certain standards are starting to emerge concerning intellectual property (IP) issues linked to outputs of generative AI, the unique challenges posed by autonomous AI should be carefully addressed.
With the increased demand for AI training from clients, what specific areas are they focused on learning?
Overall, clients want a deeper understanding of AI technology so that key stakeholders can assess the related risks, including legal, compliance, privacy, IP and HR. As with any new technology (think of the emergence of “cloud” when it was new), clients are working to understand the risks and implement processes and guidelines across their enterprise for effective mitigation strategies.
Examples include putting in place IT policies as to when and how generative AI can be used for code development, policies regarding access to and use of company proprietary and third-party data, and policies regarding automated decision-making and related safeguards.
Considering the pace of technological advancement, how can businesses future-proof their legal strategies for AI in tech and sourcing over the next three -to five years?
Adopting AI provisions in technology procurement agreements will help businesses better understand and address how AI is used for new agreements. Clients will also need to address how AI is being used across the supply chain, including in legacy relationships, which will require investigation and possible contract revisions. Implementation of guardrails will help inform key stakeholders of AI usage. Additionally, businesses should establish a specific data asset management function to address how AI solutions process and consume data.
How is Loeb a leader in the space?
Loeb has been addressing AI and automation solutions since before the recent surge in news coverage and public conversation on generative AI. We have been involved in transactions establishing joint ventures for the creation of AI solutions, using AI to enhance health care and outcomes, creating agreements for the development and enhancement of AI solutions, and implementing AI both as stand-alone solutions and as key components of outsourcing transactions.
The firm has also been at the forefront of creating template contract language for clients across industry verticals, including health care, retail, insurance and financial services. This language helps clients assess how and where AI is implemented and assists them in navigating the evolving legal landscape to address privacy, IP and compliance issues related to various AI solutions, including machine learning, deep learning and generative AI.
