Have you considered how AI due diligence for lawyers can make or break an AI product launch? AI products rarely fail because of a single bad decision. They fail because no one stopped to ask the right questions at the right time. By the time an issue surfaces, whether it’s bias in the output, a surprise regulatory restriction, or a lawsuit over data use, fixing it is far more expensive than preventing it.
That is why due diligence for AI is not just about technical audits or legal sign-offs. It is about building a step-by-step conversation between engineers, product managers, and counsel, one that starts long before launch day. Following a clear process for AI due diligence for lawyers turns compliance from a hurdle into a strategic advantage.
Step One: AI Due Diligence for Lawyers Starts With Knowing Your Model and Data
You cannot protect what you do not understand. Start with a clear explanation of the AI model’s architecture and purpose. Is it a generative model creating new content, or a predictive model analyzing patterns? Where did the training data come from, scraped from public sources, purchased under a license, or generated in-house?
This is where you uncover hidden risks. Data may carry bias from its source, or it may include personal information protected by privacy laws. If you do not ask now, you will be answering regulators later. For example, the EU AI Act already requires transparency in high-risk AI models, and U.S. sector-specific rules are quickly evolving.
Step Two: Map the Legal Landscape
AI regulation is not static. It is a moving target, with the EU AI Act, sector-specific U.S. rules, and emerging frameworks in Asia and Latin America. Map the jurisdictions that will touch your product and what those laws require. Some focus on high-risk systems, others on explainability, and still others on the human right to contest automated decisions.
At this stage, a good product counsel becomes the bridge between legal requirements and engineering design. If the law says “explainability,” the engineers need to know exactly what kind of documentation, logs, or user disclosures that implies.
Step Three: AI Due Diligence for Lawyers in Bias & Accuracy Testing
A model’s performance metrics are not just technical bragging rights; they are legal and reputational safeguards. If your AI system works well for one demographic group but poorly for another, you are holding a discrimination claim in your hands. Accuracy is equally important.
Due diligence means running tests designed to find weaknesses, not to confirm the system works perfectly. In AI due diligence for lawyers, this stage is critical for documenting fairness and compliance.
Step Four: Assess Explainability and Transparency
Some AI systems operate as black boxes, producing results without any traceable reasoning. This may be acceptable for recommending songs or films, but it will not pass muster when making financial, medical, or hiring decisions.
Ask whether the model’s decision-making process can be explained to a regulator, a judge, or an end user. If the answer is no, then you need mitigation strategies: simplified explanations for non-technical audiences, technical documentation for auditors, and clear disclosures in user agreements.
Step Five: AI Due Diligence for Lawyers in Data Governance
Data governance is not just an IT concern. It is a legal risk vector. Who owns the model’s output? Can it be shared, reused, or sold? What happens to user data once the system processes it?
Ensure the product has data minimization policies, retention schedules, and clear contractual terms with any third-party vendors or data sources. The time to negotiate IP and privacy clauses is before the system launches, not after someone complains.
Step Six: Plan for the “What If”
Every AI product needs a contingency plan. What happens if the model produces harmful or illegal outputs? How will you handle user disputes? What if a regulator demands an audit?
A good due diligence checklist includes a crisis playbook, complete with designated decision-makers, legal review protocols, and communication strategies. Hope for the best, plan for the worst, and rehearse for both.
The Launch Litmus Test
Before you greenlight an AI product, you should be able to answer “yes” to three simple questions:
- Do we understand exactly how this AI works and where its data comes from?
- Can we demonstrate compliance with every relevant regulation, now and in the near future?
- Are we prepared to explain, defend, and, if necessary, fix the system in real time?
If any answer is “no,” you are not ready to launch.
AI due diligence is not a one-off task. It is a continuous process, evolving alongside the technology and the law. For product counsel, this checklist is not just a compliance tool — it is the framework that turns risk management into a competitive advantage.
When done right, due diligence does more than prevent problems. It builds trust, strengthens your product’s market position, and lets you innovate with confidence.
