Generative AI (GenAI) is no longer just a curiosity for litigation teams. If they aren’t already using GenAI, many lawyers and litigation professionals are preparing to implement this technology to enhance their workflows. Enhancing case management processes with GenAI for litigation teams can automate repetitive tasks such as case chronology creation, transcript and document review, and deposition designations, allowing lawyers to hone in on the intelligent work of creating a winning case strategy.
In response to the growing demand for GenAI solutions, 90 percent of litigation support professionals recently surveyed said they are developing protocols and guardrails for the implementation of GenAI at their law firms. While litigation teams don’t want to miss out on the benefits of GenAI, these teams and their firms may have lingering concerns about ethics, data privacy, and how to select a transparent AI model that addresses bias and accuracy issues.
Whether your litigation team is already using GenAI to enhance case management or is preparing to implement a solution, here are some important best practices for evaluating transparency, ethics, and security.
For more about using AI in litigation, download: Harnessing AI for legal case management.
Selecting accurate and transparent AI
A critical consideration when selecting a GenAI solution for litigation teams is researching the accuracy and transparency of the AI model. Lawyers will naturally want to know whether AI is generating accurate information and how the machine reached its conclusions. Lawyers will expect their AI tools to back up their generated conclusions with verifiable evidence, just as they would when presenting information to a court or in a legal filing.
A key feature of GenAI solutions for litigation teams is “white box” analysis. This is a system where the algorithms, logic, and decision-making process of the “box” are transparent and comprehensible. Imagine it as a box with visible internal components. Because of this transparency, users can understand how the AI makes decisions and comes to conclusions, which gives them insight into its workings. On the other hand, black-box AI conceals its decision-making process, akin to a locked box; although it generates predictions or results, it withholds the methodology behind its calculation.
AI hallucinations—or when a large language model (LLM) perceives patterns or objects that are nonexistent, creating nonsensical or inaccurate outputs—aren’t an issue when white-box AI is deployed. When white-box AI performs an analysis on documents, it also makes suggestions or recommendations. When doing so, some of these AI tools provide their sources so that there is full transparency and visibility around everything the AI is doing and can be checked for accuracy to ensure that the AI is not hallucinating.
Using AI ethically
In addition to accuracy concerns, litigation teams may also have questions about whether the use of AI for lawyers is ethical in their practice. For example, can lawyers trust that the AI is only analyzing information that is relevant to the matter they are working on — and can they trust that case information will not be used by a LLM to generate responses regarding another case at the firm, or even another firm using the same AI solution?
To prevent a potential breach of sensitive information, litigation teams should select AI models that ensure the data sent to LLMs is not stored for use in training their models. This prevents data from your matters from appearing in an answer to an unrelated query. Look for AI models that allow your firm to control the databases that feed the LLMs so that queries regarding one matter only analyze that case’s material and don’t generate a response using information about a separate matter.
In addition, while there are known to be inherent biases in LLMs, white box AI models can be configured so that they only analyze and generate responses based on the content that users provide, rather than general knowledge that can originate from unknown or unverified sources. A white box approach enables developers and users to examine, validate, and even alter the AI’s behavior for accuracy, fairness, and ethical considerations.
Keeping client data secure
Maintaining the confidentiality and integrity of client documents is paramount for law firms and a key consideration when litigation teams consider whether they can feed this information into LLMs. When choosing an AI solution, check to ensure that:
- The content your firm feeds into LLMs will not be shared between cases
- Your firm’s content will not be used by LLMs for learning or any other purposes
- Outputs generated by LLMs will be owned by your firm
- No data will be processed by AI unless a user at your firm specifically submits it
- Your firm’s lawyers will ultimately have the final say before any data is saved permanently
Conclusion
GenAI helps lawyers identify relevant document data faster and more efficiently, but only if the AI model integrates these layers of transparency, ethics, and security features. Lawyers must be confident in all information included in their work product. By selecting AI tools that support them with accurate, relevant, and helpful data, they can better manage the growing waves of incoming data and confidently use AI to enhance their own perspective, knowledge, and experience.
