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Generative AI and Large Language Models are fast emerging as transformative tools with vast applications across industries, including legal practice. The pace of adoption is happening at a breathtaking clip: ChatGPT reached 100 million users just two months after its launch, one of the fastest-growing internet applications in history. With generative AI's uncanny ability to generate human-like content, including text, images, and videos, the technology's opportunities (and risks) are stirring the public's imagination.
Routine tasks like legal research, contract review, and drafting represent some of the most common applications of generative AI in the legal sector. However, apprehensions about accuracy, data, and reliability hinder widespread adoption. The legal industry demands a sophisticated, professional-grade solution to overcome these hurdles. What does generative AI mean for the future of legal practice? And what can legal teams do to harness generative AI's promises and avoid risks?
The legal profession has long been identified as one of the most ripe industries for AI transformation. Researchers at Princeton University, the University of Pennsylvania, and New York University pegged legal as the most exposed to AI disruption. An often-cited report from Goldman Sachs estimated that 44% of legal work could be automated - second only to office and administrative support jobs. Indeed, skills like reading, analyzing, and summarizing are fundamental legal skills. And generative AI's capability to generate language that mimics human-like writing opens doors for automating numerous tasks.
One significant application is in contract review, an often time-consuming and repetitive aspect of legal work. Automating such tasks with generative AI and other AI techniques could make the process faster and more efficient, freeing legal professionals to focus on more complex, higher-value tasks.
It is unlikely that AI will displace or fully automate legal work any time soon - the work of lawyers requires nuanced, complex human judgment. AI, instead, promises to free up time for legal professionals to focus more on tasks that require human judgment. 8 out of 10 legal professionals agree that there are many tasks that the technology can be applied to, according to Thomson Reuters. And many believe that the effective use of generative AI will separate successful lawyers from unsuccessful ones, according to a survey by Above the Law and Wolters Kluwer.
In a recent LegalOn Technologies and Artificial Lawyer survey, 67% of legal professionals reported being "very excited about the potential benefits of AI in legal," 32% of legal professionals stated they are "cautiously optimistic about AI in legal for some use cases." Respondents were from law firms, corporate legal departments, technology companies, and consultancies.
As legal professionals become increasingly aware of the benefits of generative AI, many are seeking potential ways to use it – and considering the potential risks in doing so. Despite the enormous potential, it's essential to recognize that the application of generative AI is not without challenges. These pitfalls are particularly pertinent in legal practice, where accuracy, compliance, and data protection are paramount.
AI Hallucinations: AI hallucinations occur when a generative AI model generates false but confident-sounding content. A notorious consequence of this risk is when two NY lawyers included six fictitious case citations from ChatGPT in a legal brief. For contract reviewers, these hallucinations can inject contracts with unforeseen risks that could jeopardize a client's interest and a legal professional's reputation. Legal professionals should supervise AI-generated content and work with vendors that rigorously test, validate, and put in place guardrails to prevent it.
Data Privacy, Cybersecurity, and Compliance Risks: Legal contracts often include sensitive, if not confidential, data. Without proper safeguards, using generative AI in contract review could expose this data, leading to data privacy, cybersecurity, and compliance risks. A recent study by LayerX indicated that 6% of workers had sent sensitive data to ChatGPT. To mitigate these risks, data protection measures should be in place, ensuring that no contract data is used for training generative AI models and no data is stored by third-party providers of LLMs.
Ethical Considerations: A broad range of ethical duties govern a lawyer's use of any technology, including generative AI. ABA Model Rule 1.1 requires a lawyer to provide competent representation, including a duty to learn about the benefits and risks with new technology. Lawyers must also ensure that they preserve confidentiality, adequately supervise AI use, and disclose, when appropriate, its use to clients and courts. The State Bar of California and the Florida Bar each issued guidance regarding the use of generative AI.
Ensuring Quality and Scalability: One of the challenges of deploying generative AI in legal practice is finding the right balance between producing high-quality, context-specific results and maintaining scalability. For instance, generic limitation of liability clauses that ignore the context of the contract and the parties are insufficient for legal professionals. Overcoming this obstacle calls for intricate engineering of prompts and extensive training of AI models on databases rich in legal content or working with vendors who do.
In the LegalOn and Artificial Lawyer survey, over half of the attendees cited accuracy as the biggest barrier to adoption in their organization.
Extensive Testing with Expert Validation: Legal teams should ensure their AI vendors rigorously test generative AI tools for accuracy and reliability. Conducting thousands and thousands of tests before releasing a GenAI tool to users is table stakes. But when dealing with legal content generation, it's best to ensure that a vendor's testing is done by qualified legal professionals intimately familiar with contract writing, legal nuances, and legalese.
Maintaining Control over AI: It's crucial that legal technology vendors maintain strict control over their use of LLMs to protect against AI hallucinations. Out-of-the-box models, without additional guardrails and fine-tuning, are ill-equipped for the complexities of contract review.At LegalOn, we've spent 6+ years developing large databases of rich legal content authored by experienced lawyers. This allows us to coach the LLM to produce professional-grade outputs consistently. Moreover, we've fine-tuned our system settings through large-scale experimentation to control for LLM variability and creativity.
Protect Confidential Information: Contracts are among your most sensitive business documents. Ensuring security and confidentiality is critical. That's why technology providerslike LegalOn use secure and privacy-compliant platforms like Microsoft's Azure OpenAIService, ensuring that sensitive information is not used to train generative AI models. Legal teams should verify their technology providers have robust data privacy protections in place to prevent unauthorized access or use of proprietary and sensitive data, including security certifications like SOC 2.
Empowering Human Decision-Making: Despite the advancements in AI, final decision-making should always rest with human professionals. It's important to avoid tools that fully automate contract review without providing the opportunity for human oversight. Tools like LegalOn are designed to support legal teams by highlighting risks and suggesting revisions but leave the final decisions to the human experts. Legal teams should ensure that AI tools are used to augment human expertise, not replace it, ensuring that legal judgments remain nuanced, context-aware, and ethically sound.
AI is the buzzword, and many entities are rushing to integrate it without a clear roadmap. However, a responsible platform doesn't just add AI; it understands it. LegalOn's journey since 2017 showcases a conscious commitment to investing in AI and applying it responsibly, ensuring that the technology truly enhances the user's experience without compromising accuracy or ethics.
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