Universities are using AI to boost tech commercialization, but hidden legal and governance challenges may limit gains. Success depends on combining new AI tools with strong human judgment.
Universities are adopting artificial intelligence (AI) to accelerate technology commercialization. AI promises efficiency, but a complex network of legal, technical, and governance caves lies beneath the productivity surface. And the costs of navigating these risks may surpass promised conveniences. Understanding and mitigating the benefits of AI at the complex intersection of technology, business, and law is important to capturing the net benefits. The solution lies in strategic matching of modern tools with legacy human approaches.
The Deception of Accuracy
Generative AI tools claim to minimize "hallucinations" or false outputs based on nonexistent patterns the AI perceives. But many AI tools still fabricate critical information. False data compromises legal opinions and patent applications, which creates liability for the organization and its stakeholders. "Misgrounding" is another issue, occurring when a tool cites an existing but irrelevant source to support a false statement. Detection of either error requires additional layers of often expensive deep subject matter expertise.
Technical Barriers to Clarity
The technology transfer environment creates unique opportunities for high error rates. Even human specialists struggle to accurately transcribe technical jargon and scientific terms rooted in Greek, Latin, etc. At training, general consumer and business AI tools train on speculation and misapplications of basic scientific principles from social media and news bulletins, which increases the risk of inaccurately processing frontier science. At inference, they equally capture speculation and sarcastic remarks as much as authoritative statements. They can also misinterpret rich sources of tone, context, and nonverbal cues. The result is a questionable and poor record for decision makers at best, and legal liabilities at worst.
The Discovery Trap
AI summarization changes the discovery profile of an organization. Courts could find AI-generated summaries discoverable in litigation. This applies to internal candid conversations, unwarily preserved for opposing counsel. Default summary options bypass carefully crafted document retention policies. Using third-party vendors also risks waiving attorney-client privilege that requires strict confidentiality between attorneys and clients. A vendor presence in a digital meeting may waive this protection. Opposing counsel may use an unreliable AI summary to damage credibility or muddy otherwise clear scenarios.
Inventorship and Privacy
AI summarization introduces new technical limitations into the record. U.S. patent laws only allow natural persons to qualify as inventors, so an AI system as an “inventor” affects protection strategies. Some AI platforms use customer data to continuously train their AI models. This can create an unacceptable risk in a practice that relies on novelty and non-obviousness over prior art. They can also fuel privacy risks that violate state and federal laws. Federal and state privacy laws still exist for improperly recording private conversations without the consent of all parties.
The Human Mandate
Savvy organizations integrate modern AI tools intentionally. Technology Transfer Offices must be strategic so technical and legal professionals can move forward confidently. Thriving offices use AI as a tool with defined governance principles. This is especially true for consumer-grade or general language-trained tools. Crafting workflows that advance organizational goals while maintaining standards is not intuitive. Human oversight is integral to the productivity path.