Navigating the New Frontier: Trusting Generative AI in Legal Work
Generative AI is ushering in the next era of legal work. The emergence of generative AI, and the prospect of augmenting the work of legal teams, has ignited both excitement and pragmatism in the industry.
A recent Thomson Reuters survey offers a compelling insight into this blend of perspectives. An overwhelming 82% of legal professionals surveyed believe in the value of generative AI technology in legal work. 59% of legal partners and managing partners agree that generative AI should play a foundational role in the industry.
Despite this enthusiasm, the journey towards fully integrating generative AI in legal workflows is paved with cautious optimism. The same survey revealed that 62% of respondents harbour concerns regarding the deployment genAI technologies. A notable point of apprehension lies in the handling of confidential client data, with all participants indicating a hesitance to entrust generative AI tools fully with such sensitive information.
As law firms navigate this new frontier, the conversation shifts from whether to integrate generative AI into legal practices to how to do so in a way that upholds the profession's highest standards. The industry's eagerness to adopt AI is matched only by its commitment to ensuring that these technologies are used responsibly, ethically, and effectively, reinforcing the indispensable role of human judgement in the legal process.
The question therefore remains – what should generative AI be used for in legal work? Can it, indeed, be trusted to reliably and practically draft legal documents such as contracts? And what of generative AI summarisation tools? Can they be trusted?
In this article, we delve deeper into the technology developments that are improving the outcomes, trustworthiness, reliability and applicability of generative AI in the legal sector.
Can you trust generative AI for legal work?
At its core, generative AI refers to algorithms capable of generating novel outputs from the data they've been trained on. It can generate new content based on learned patterns, summarise large documents and interpret trends from unstructured data. All with limited guidance.
Among the most compelling use cases is the drafting of documents and contracts. Leveraging generative AI, law firms can automate the creation of complex legal documents, ensuring they are both high-quality and consistent with current laws and legal precedents. This not only saves time but also reduces the potential for human error, enhancing overall efficiency.
The hallucination problem in genAI
One of the critical issues facing generative AI in legal contexts is the phenomenon known as "hallucinations." These occur when an AI generates information or data that, while plausible, is not accurate or based on factual inputs. Hallucinations can result from overgeneralisation or misinterpretation of the training data, leading to outputs that could potentially mislead or result in erroneous legal advice or documentation.
Given the sensitive nature of legal work, which often involves confidential client data and the need for precision, it's crucial that the generative AI tools employed by law firms include built-in accuracy, reliability, and hallucination protections. These safeguards ensure that the AI's outputs are not only useful but also reliable and secure, preventing the use of incorrect information and protecting client confidentiality.
Safeguarding, ethics, privacy and data security in genAI
Generative AI, with all its benefits, introduces new considerations for security, data privacy, and accuracy. To safely harness its power, legal professionals must look for AI tools equipped with specific features that safeguard operations and client data.
Generative AI Safeguarding – the five critical features
1. Ethical Walls.
Implementing ethical walls and sophisticated user permissions within AI tools is crucial for maintaining client confidentiality and ensuring that sensitive information is accessible only to authorised personnel. These features prevent potential conflicts of interest and ensure compliance with legal standards. This prevents data ‘leaking’ into unexpected responses, and it means your prompts and data are not used to train an algorithm.
2. Sharing, User Permissions & User Traceability.
Traceability features allow for the monitoring and recording of all interactions with the AI system. This capability is vital for maintaining accountability, conducting audits, and ensuring that all actions can be traced back to individual users, providing a clear record of data handling and decision-making.
3. Enterprise-grade security.
To protect sensitive legal information, generative AI tools must employ the highest levels of security. This includes encryption and authentication protocols that ensure data integrity and confidentiality, safeguarding against unauthorised access or breaches.
4. Deployment flexibility & data residency.
Recognising the global nature of legal work, AI tools must offer deployment flexibility, allowing firms to choose where their data is stored and processed. This is particularly important for complying with varying international data protection regulations and ensuring that data residency requirements are met.
5. Siloed algorithms.
To tailor the AI's outputs to specific legal tasks and ensure accuracy, it's important that the underlying algorithms can be customised or "siloed" for different use cases. This approach prevents the cross-contamination of data and maintains the integrity of the AI's outputs.
