When Accurate AI Is Still Dangerously Incomplete

What Are the Limitations of AI Accuracy?

As businesses increasingly rely on artificial intelligence (AI) for decision-making, accuracy often takes center stage. However, in complex sectors like law, accuracy alone falls short. The stakes are high; incorrect or incomplete information can lead to severe consequences. In this post, we will examine why AI accuracy is often dangerously incomplete and how organizations like LexisNexis are tackling these challenges.

Why Is Legal AI So Complex?

Min Chen, LexisNexis' SVP and Chief AI Officer, states, "There's no such thing as 'perfect AI' because you never achieve 100% accuracy or relevancy, especially in high-stakes domains like legal." This statement underscores the challenges of ensuring that AI outputs are not only accurate but also comprehensive and trustworthy.

In legal contexts, an incomplete response can be more damaging than no response at all. If a user poses a question involving multiple legal considerations, and the AI only addresses part of it, the result can be misleading. This highlights the critical need for completeness in AI outputs.

How Can We Ensure AI Outputs Are Complete?

To effectively evaluate AI models, LexisNexis has developed various sub-metrics that assess "usefulness." Key metrics include:

• Authority: Is the information from a credible source?
• Citation Accuracy: Are the references valid and applicable?
• Hallucination Rates: Does the AI generate false or misleading information?
• Comprehensiveness: Does the response cover all aspects of the user's question?

Chen emphasizes, "It's not just about relevancy. Completeness speaks directly to legal reliability." Failing to provide a complete answer can lead to real-world risks, making it essential for legal AI tools to excel in this area.

How Is LexisNexis Advancing Legal AI?

In 2023, LexisNexis launched Lexis+ AI, a significant advancement in legal AI capabilities. Initially built on a standard retrieval-augmented generation (RAG) framework, this tool integrates a hybrid vector search that grounds responses in a trusted knowledge base. In 2024, they introduced Protégé, a personal legal assistant that incorporates a knowledge graph layer to enhance the quality of AI-generated responses.

Traditional semantic search may yield contextually relevant content but often lacks authoritative answers. Chen explains that initial returns must be filtered through a "point of law" graph to ensure users receive the most reliable documents. This innovative approach addresses a key limitation of basic AI frameworks.

What Are Agentic Graphs and Planner Agents?

LexisNexis is also developing agentic graphs that facilitate complex, multi-step tasks. These advancements include:

• Planner Agents: Break down user questions into sub-questions for more refined responses.
• Reflection Agents: Critically assess their outputs, enabling real-time improvements.

This collaborative approach between human experts and AI agents promotes deeper integration of technology in legal practices. As Chen emphasizes, "I see the future as a deeper collaboration between humans and AI."

Why Should Enterprises Care About These Developments?

The insights from LexisNexis provide actionable strategies for businesses looking to implement AI in high-stakes environments:

1. Prioritize Comprehensive Metrics: Establish a framework to assess AI outputs beyond accuracy.
2. Invest in Advanced Frameworks: Move beyond basic RAG models to incorporate knowledge graphs and agentic structures.
3. Foster Human-AI Collaboration: Encourage a symbiotic relationship between legal professionals and AI technologies.
4. Cite Authoritative Sources: Ensure that all references used by AI models are up-to-date and reliable.
5. Embrace Continuous Improvement: Foster a culture of experimentation and iteration to enhance AI effectiveness.

What’s Next for AI in Legal Practices?

The journey toward accurate and comprehensive AI is ongoing. While accuracy is crucial, it is not the only factor that determines the quality of AI outputs in complex domains like law. By focusing on metrics that assess authority, citation accuracy, and comprehensiveness, organizations can better manage the inherent uncertainties in AI. Companies like LexisNexis lead this evolution, demonstrating that the future of AI lies in collaboration between human intelligence and advanced algorithms. As businesses navigate this landscape, understanding and addressing the limitations of AI will be vital in delivering consistent value and reliability to customers.

Watch the full podcast to explore how LexisNexis is reshaping the landscape of legal AI and gain insights on effectively integrating AI strategies.