Generative AI Implementation for Lawyers: IP, Data-Protection & Contracting Risks

Artificial intelligence (AI) initiatives are racing from R&D labs into everyday use and the legal implications are just as fast-moving. In this webinar, veteran AI practitioner Dr. Phil Winder joins Mills & Reeve technology-law partner, Paul Knight, for a candid conversation about what really happens when organizations decide to “do AI”. This interview includes:

how generative models differ from traditional machine learning systems
the risks that keep counsel awake at night
proven guardrails to tame hallucinations and brand damage
contract structures that reconcile agile delivery of AI projects with robust legal protection
real world case studies and disasters

Legal AI Webinar Notes

AI Implementation Landscape

Post-ChatGPT, interest in AI has spread from tech teams to “everyday” professionals: lawyers, doctors, risk managers, etc.
Generative AI (Gen AI) and large foundation models dominate current discussion, but traditional machine-learning projects continue in parallel.
Organisations often approach vendors with only a vague desire to “do AI,” masking very conventional automation needs.

Clarifying Objectives Before Deploying AI

Many “AI” requests turn out to be simple workflow problems solvable without ML; discovery conversations are essential.
Start by mapping the business problem, expected ROI and risk profile before deciding whether AI is justified.
Be prepared for vendors to recommend non-AI solutions, good advice may “talk them out of a job.”

AI Legal Risks

Awareness levels track industry exposure: high-risk domains (finance, health, law) are more attuned; low-risk domains often overlook hazards.
Core AI legal risks:
- AI IP infringement
- Inaccurate or harmful output (brand, contractual or safety consequences)
- Data-protection breaches and loss of control over personal data.

Intellectual Property & Training Data

Foundation-model suppliers seldom disclose full training corpora. Data curation is their competitive moat.
Transparency varies along a spectrum: closed SaaS (OpenAI), downloadable “open-weight” models, and fully open-source stacks.
Contractual route: shift infringement risk to suppliers via IP indemnities; practicality depends on your bargaining power.

Accuracy, Hallucinations & Brand Risk

Notable failures: Microsoft Tay’s 24-hour descent into hate speech; chatbots at Virgin Money and DPD insulting customers; Air Canada refund case enforced by court.
“Hallucination” stems from probabilistic text prediction, not intent; expect confident fabrications unless controlled.
AI hallucination mitigation can be achieved through a variety of “fact-checking” techniques.

Risk-Mitigation Strategies

Human-in-the-loop review for borderline or high-stakes decisions (e.g., credit approval).
Guardrails & moderation layers to filter prompts and responses.
Retrieval-augmented generation (RAG): ground answers in a vetted document store and cite sources.
Robust testing & monitoring: treat prompts and model upgrades like code; establish roll-back plans.
Explicit disclaimers can limit liability (worked for OpenAI in U.S. defamation suit).

Data Protection & Sovereignty

Terms of service range from “we log and retrain on everything” to fully private, on-prem deployments; choose based on sensitivity.
Cloud providers offer middle-ground “no-training” modes, but jurisdictional laws (Patriot Act, UK equivalents) still apply.
Highly risk-averse organisations keep models on self-hosted hardware inside national borders.

Regulatory Environment

EU AI Act Compliance:
- Unacceptable risk: banned.
- High risk: strict obligations (e.g., safety-critical products, law-enforcement, education).
- Limited/minimal risk: lighter touch (chatbots, emotion recognition).
UK taking “wait-and-see” approach; recent Data-Use & Access Act dropped copyright-training amendments but government promises a report within 9 months.
More regulation = higher development cost; global tooling complicates compliance because users, compute and developers span jurisdictions.

Project Delivery & Contracting Approaches

Waterfall fails for AI due to research uncertainty and long feedback loops.
Use Agile / phased statements of work: Proof-of-Concept, MVP, production, support.
Master Services Agreement with modular SOWs reconciles supplier agility with procurement’s need for cost control.
Consider leveraging published model clauses with AI contracting best practices:
- Society for Computers & Law AI Contract Clauses
- EU Public Buyers’ “full” and “light” AI procurement clauses

Case Study: Temple University Legal Research Tool

Problem: fragmented U.S. housing law took weeks/months for researchers to interpret.
Solution (2021): Gen AI system that indexes statutes, surfaces relevant passages, assists query formulation and answers questions with citations.
Outcomes: dramatic research-time reduction; public-interest impact; early example of domain-specific, RAG-style Gen AI in law.

In-House Counsel AI Guidance

Interrogate objectives first: be sure AI is the right fit.
Demand (and read) supplier Ts&Cs: indemnities, data use, retraining rights, logging.
Match controls to risk: high-risk use cases need explainability, audit logs, human oversight.
Check jurisdiction chains: where does data travel: which laws apply?
Adopt agile contracts: allow iterative delivery without sacrificing legal protections.
Consult model clauses as a starting point; tailor for IP, data protection and regulatory tier.