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The rapid growth of Artificial Intelligence technology used across any given business complicates contractual agreements with 3rd parties as well as mergers and acquisitions (M&A) transactions. One critical issue is data ownership and licensing-related risks (orrick) which may not be currently addressed in existing contractual and legal review processes or in M&A due diligence activities. These risks are training data, data quality, clarity of ownership, and use limitations. The risks and our thoughts on how to navigate and mitigate these strategies are below.
Training Data
Documentation & Ethically Sourced Data - AI systems heavily rely on training data, which is often scraped or open-source data where ownership and data usage rights are not clear. Understanding and documenting training data flows is the first step toward ethical and effective use.
Data Quality
Internal Quality Controls & Formalized Audit Systems are needed - AI data pollution (McKinsey) occurs when the data used to train or operate AI models is flawed, incomplete, or biased, potentially leading to biased predictions, unreliable recommendations, and inaccurate insights.
Clarity of Ownership
Thorough Disclosures & Updated Due Diligence Processes Across Acquisition Teams - Determining ownership of training data can be complex and uncertain. It might be subject to claims by third parties, infringement claims, privacy issues, or other legal restrictions. This uncertainty could impact not only the use of training data but also ownership of algorithms built using that data and any synthetic data created.
Use Limitations
Commercial Use - If training data has use limitations, it can restrict how a company commercializes and licenses the data, develops technology, and applies algorithms, limiting the potential value of acquisitions.
As experts in Enterprise Risk Management and Management Consulting with over 30 years of experience, we are constantly seeking to identify key and emerging risks to help businesses best navigate rapidly evolving technology and strategies.
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