The Key Factor to Consider When Choosing Disruptive Legal Technology for Your Firm

Challenges abound for corporations managing their eDiscovery workload today. There are data challenges in terms of the Big Data volume and variety from more data sources than ever. There are risk challenges in terms of increasing threats from cyberattacks, ever-changing data privacy laws to raise the stakes on protecting sensitive data, and selecting the right technology to support their needs. And there are budgetary challenges, as corporations are under increasing pressure to do more with less.

Corporations can’t afford to select outside counsel firms that continue to do things “the way we’ve always done them.” To remain competitive and valuable to clients, lawyers today need to evolve and embrace disruptive technology that enables those clients to reduce the life cycle of a case, address mounting data volumes and new data types, and keep your client’s sensitive information secure.

Becoming a disruptive lawyer, however, doesn’t mean simply embracing the latest tech, even if everyone is talking about it. The key factor to consider when choosing disruptive technology for your firm is determining whether the tech has been proven to reliably meet clients’ needs and if the technology provider has a track record of being a partner who actively problem solves, innovates quickly, and has the track record to prove it. Choosing proven disruptive tech is the key factor in innovating for your clients, while doing so in a manner that minimizes risk.

The Biggest Risks of Generative AI

Unless you live under a rock, you’ve undoubtedly heard about generative AI (GAI) tools such as OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Bard. ChatGPT is so good at generating high-quality content that the use of it has been banned by several schools and colleges. GPT-4 has even recently passed the Bar Exam — by “a significant margin!” These tools have powerful capabilities and great promise.

The remarkable promise of GAI tools like ChatGPT is tremendous and we’re starting to see software companies explore this disruptive AI technology’s potential use for eDiscovery. However, there are also several significant risks posed by generative AI today that your firm should consider before jumping in to apply this tech to eDiscovery use cases. In our whitepaper, “The Disruptive Lawyer,” we identified the four biggest risks of generative AI:

• Data Privacy: Data privacy is among the biggest concerns since user data is often used to train the underlying models of GAI, even if those users never explicitly give their consent.
• Hallucinations: If you’ve used GAI chatbots, you’ve probably noticed they’re prone to factual inaccuracies or “hallucinations,” which makes them unreliable for research purposes and creates serious liability risks.
• Copyright Issues: GAI models are trained on large quantities of internet data — including copyrighted materials — that are used to generate an output. This means works not explicitly shared by the original source can be used to generate new content.
• Cybersecurity: Cybercriminals can use GAI to build malicious code and create more advanced phishing threats than ever. Yet, many organizations have not implemented security measures to keep up with these growing threats.

Risks like these are why we think it’s best to tread lightly when it comes to their application to eDiscovery use cases. There’s a big difference between leading edge and “bleeding edge.” In addition to these risks, there are also the added costs for utilizing these technologies because they are so resource intensive.  

Examples of Proven Disruptive AI Technology

While generative AI may not be ready yet for “prime time,” there are examples of proven disruptive tech today that your firm can leverage to address your client’s data and budget challenges, while also mitigating risk. Here are six examples of proven disruptive technology in the legal industry you can use today to benefit your clients:

• Active Learning: While GAI tools haven’t been proven to deliver reliable results yet, you can still apply machine learning to your discovery collection to continuously predict and rank unreviewed documents. The technology and methodologies associated with active learning have been in use and proven for over a decade to defensibly expedite analysis and review of your document collection.
• Dynamic Batching: Automatically batching documents for review based on their ranking determined by the active learning process ensures that the most relevant documents continue to advance to the front of the review queue.
• Entity Recognition: The ability to analyze documents to extract pertinent data regarding important places, organizations, products, people or other common data categories can be useful for identifying important documents to review and others to exclude from review.
• Data Stories: Understanding any dataset starts by understanding the entities (i.e., people and organizations) dates and key terms within that dataset. The ability to visualize relationships to build “stories” within the data can streamline the process of understanding the key facts represented by that data.
• Clustering: Document clustering is proven technology to create review efficiencies by identifying and grouping similar documents, which can facilitate which groups to prioritize and which to set aside.
• Email Thread Identification: The ability to identify the emails within a thread and limit review to only the most inclusive message(s) within the thread (while preserving the ability to apply tags to the entire thread) saves considerable time in review while reducing inconsistent classifications. 

Ryan O’Leary, Research Director of IDC, says that while a lot of attorneys are curious about all the ways they can leverage the new AI technologies out there, “It’s important for legal professionals to leverage only the ones with a proven track record of creating optimal legal outcomes for their clients and their firms.”

We agree. While it’s important to stay abreast of changes in the technological landscape and consider how those changes will eventually lead to beneficial disruptions in eDiscovery workflows, the key factor to consider when choosing disruptive technology for your firm is track record. Sticking with proven disruptive tech will enable your firm to address your clients’ data and budget challenges while also avoiding risk challenges associated with unproven technologies. Ultimately, proven disruptive technology will empower you to be the disruptive lawyer your clients need.

To learn more about how proven AI technology, such as Casepoint’s AI suite called CaseAssist, can help you overcome your most complex cloud challenges, download our whitepaper now.