The Future Of Legal: Combining AI And Human Expertise For Improved Outcomes
Humans must still be in charge of checking the results to validate accuracy.
Predictive coding uses AI to accelerate legal information review, saving significant time and effort. However, its results are only as good as the efforts put forth by the human lawyers who train its algorithms, assess its output quality, determine the relevance of the evidence it finds, and decide the next steps forward in a legal matter.
As we look at how its capabilities help reduce corporate risks and improve legal outcomes, we’ll also see how embracing AI with a human-centric approach can help you lead your legal team to feel confident in a more efficient and productive future.
Predictive Coding Algorithms Need Human Guidance
Predictive coding’s primary function is to make the growing amounts of unstructured data, such as documents, emails, instant messages, images, videos, and audio files, much more accessible for lawyers to understand and analyze.
In one method, an experienced lawyer or subject matter expert initially trains a machine learning algorithm on a subset of documents that have been manually reviewed and coded for relevance. In a more recently developed continuous active machine learning process, the algorithm uses AI to train itself.
In both processes, the system singles out the most relevant documents and prioritizes them for legal review. Humans must check the results to validate accuracy.
Predictive coding can review massive datasets in hours or minutes more accurately than manual review. It’s a boon for e-discovery and litigation — but what else can predictive coding do?
Improve Outcomes For In-House Legal Teams
Legal teams use predictive coding to build stronger cases and make more-informed decisions. This ultimately leads to better outcomes in litigation, M&As, and any legal proceeding where documents are a critical factor, such as:
Early case assessment and first-pass review. During the early stages of an investigation or legal matter review, lawyers can quickly pull the most relevant documents for initial review to gain a more thorough and critical understanding of the issues faster than they ever could manually. More efficient analysis of the evidence leads to faster, more-informed decisions on the next steps.
With simplified and streamlined early review capabilities, legal teams may become more likely to examine potential issues. This may ultimately help companies capture essential details and catch critical issues earlier.
Privilege review. Avoid inadvertently producing privileged documents and the accompanying sanctions and penalties. Quickly locate and redact them before production. The benefits of this will increase exponentially as legal matters increase in both size and complexity.
Refined quality control. Quality control, such as monitoring predictive coding accuracy rates, reviewing error reports, and conducting spot checks on randomly selected documents, helps ensure it properly classifies all relevant documents.
If there are errors or inconsistencies in the results, it does not signify a crashing end to the project. Rather, you adjust and retrain the algorithm and rerun it as often as necessary as discovery and investigations progress. Your team avoids missing important documents that could impact legal outcomes and business results.
Keep The Human Element In Document Review
Predictive coding is an excellent example of how AI augments rather than replaces human lawyers. Even as it propels the legal profession into a new era of productivity and efficiency, predictive coding remains human-centered. It cannot interpret legal concepts or provide legal advice. Only lawyers or SMEs can train algorithms and evaluate their output. Only human lawyers with specialized training and experience can understand complex legal issues, apply them to specific cases, and decide future steps.
Automating document review gives you more-accurate results faster. But the ethical obligations to maintain client confidentiality, avoid conflicts of interest, and provide competent representation require human judgments that cannot be delegated to machines.
How might you use predictive coding to improve legal operations in your organization?
Where else might predictive coding be practical?
How can we combine AI with human judgment to speed legal workflows?
Olga V. Mack is the VP at LexisNexis and CEO of Parley Pro, a next-generation contract management company that has pioneered online negotiation technology. Olga embraces legal innovation and had dedicated her career to improving and shaping the future of law. She is convinced that the legal profession will emerge even stronger, more resilient, and more inclusive than before by embracing technology. Olga is also an award-winning general counsel, operations professional, startup advisor, public speaker, adjunct professor, and entrepreneur. She founded the Women Serve on Boards movement that advocates for women to participate on corporate boards of Fortune 500 companies. She authored Get on Board: Earning Your Ticket to a Corporate Board Seat, Fundamentals of Smart Contract Security, and Blockchain Value: Transforming Business Models, Society, and Communities. She is working on Visual IQ for Lawyers, her next book (ABA 2023). You can follow Olga on Twitter @olgavmack.
