While generative AI may feel like a hot new topic, the legal industry is no stranger to leveraging artificial intelligence.
Generative AI is revolutionizing the legal industry by driving critical efficiencies in long-established processes. Doing more with less has historically been a challenge for in-house counsel, and teams are eager for new solutions, particularly in e-discovery. As documented in a new IDC Research Study commissioned by Relativity, Generative AI in Legal 2024, 50% of respondents have reported that their AI use has increased over the past two years and respondents reported that 48% of their day-to-day AI use involves generative AI.
Knowing where to start with generative AI can feel overwhelming, especially with so many potential applications. To help narrow the options and derive the greatest value, in-house teams need to prioritize solutions that will have the most impact on how they work. For teams ready to apply generative AI directly to e-discovery, first-pass review has proven a great area to start.
Why First-Pass Review
During a recent industry event, in-house e-discovery leaders highlighted three key reasons for why first-pass review is an ideal starting place:
In the end, experimentation is key to learning quickly and getting the most out of generative AI- powered solutions. From automating routine tasks to taking on parts of the EDRM, continuous testing and refinement finding will help you find the most effective use case for your organization.
Trust But Verify
As with any new technology or process change, corporate legal teams should approach generative AI with the same caution, identifying inherent risks and verifying outputs. When evaluating the results of generative AI, teams can depend on the same best practice metrics used in e-discovery for the last 20 years: recall, precision, elusion rate and more. By adhering to these metrics for accuracy, teams can easily gauge the performance of their prompt input and compare results to current workflows.
Generative AI does introduce new types of risks, but these risks can be mitigated with appropriate technology parameters. For example, Relativity addresses the risk of hallucinations through a chain-of-thought approach, requiring that generative AI validate its citations in the source document it is analyzing.
When it comes to security and privacy with generative AI, organizations need to deploy strategies to prevent employee misuse. Public models like ChatGPT are constantly learning, and employees can easily input proprietary data into prompts that become part of the training data set for future outputs.
Relativity
At Relativity, generative AI solutions are built with privacy and security at their core using a secure integration with Microsoft Azure's OpenAI services. All data input into Relativity aiR for Review stays within the confines of RelativityOne, and no data is stored or retained by Azure OpenAI. Additionally, all AI innovation rests upon Relativity's AI Principles, which guide our everyday decision-making to ensure we're creating technology that's clear, fair, and gives our customers the utmost control.
Starting your Journey
Like any journey, the beginning is the hardest part. Here are some tips to get started:
The next step to implementing generative AI into your e-discovery workflows is simple: set up time with the Relativity team to find the right solution or partner for your business.