What if you started typing a brief and, as you typed, case citations supporting your arguments magically appeared on the page? Sound far-fetched? Well, that’s just one of the projects of the “law+tech” movement, and it’s probably not long before that and more is possible.
Law+tech refers to the use of enhanced computer-based data and computation capacities in legal practice. Legal search engines developed in the 1970s were the first wave of law+tech, and few lawyers today could practice without them, but they will seem puny by comparison to what lies ahead. Law practice is on the cusp of a major technology-based “disruption” thanks to the development of highly effective machine learning algorithms, natural language processing, and big data management capacities. E-discovery was just the low-hanging fruit for unleashing this package of computational powers in the legal industry, as legal technologists are using them to develop new legal search tools, predictive analytics tools, and other applications that will perform tasks that currently require human lawyers to carry out.
The term “disruption,” though, shouldn’t be thought of as meaning law+tech is the enemy of lawyers. Disruption can be unpacked into several kinds of effects, and whether any of them is good or bad for a lawyer depends on the lawyer’s context and initiative.
Effects of Technology-Based Disruption
In the do it better, faster, and cheaper trilogy dominating the legal industry today, quality-enhancing technology works on the delivery of better service. For example, as legal search engines lowered research error rates, they enhanced quality.
One of the great advantages of computers is speed. Even if the error rate is no better than humans, e-discovery and legal search engines can be more efficient.
All of the above discussion has assumed it will be lawyers using the new technology, which clearly will not always be the case; the new technology might reduce or eliminate the need for a lawyer at the helm. Some new technologies will provide user interfaces that do not require an attorney to operate.
Assume a world in which the number and scope of client-driven legal searches do not change. In that case, the introduction of a new legal technology that has quality and efficiency enhancement effects is likely to displace demand for human lawyers in some sectors of the legal industry if the technology is a cost-effective competitor.
The opposite side of the coin is the potential a new technology has to open up new markets for legal tasks not previously possible or valued. For example, some of the research techniques made possible through legal search engines would have been virtually impossible to replicate the old-fashioned way (with books). To the extent these new capacities are valued (i.e., they lead to better litigation prediction and outcomes), they will increase demand for legal services.
The trilogy of machine learning, natural language processing, and big data is bound to have all of these effects as they wind their way into legal practice. For example, if the described case citation technology comes to fruition, it could result in a lawyer being able to cite better cases quicker and thereby produce a stronger brief faster. On the other hand, writing briefs might become so easy and cost-effective that in-house legal departments can do it without having to hire outside lawyers. But one can also imagine how this technology could benefit lawyers serving lower-income populations and small businesses, transforming how they access the legal system. Maybe, though, they’ll just start writing their own briefs!
The point is that the law+tech movement is happening whether lawyers like it or not—it’s inevitable. Its impacts will be uneven throughout the legal industry, opening up new opportunities and shutting down others. The young lawyers of today will be a part of a period of technological innovation like none before, and it will transform how lawyers practice. One strategy is to stay ahead of the curve by learning what is new out there and taking advantage of related opportunities. The other is too long for the good old days.
Machine learning is an automated method of data analysis and analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find patterns without being told precisely where to look. A common example is your email program’s junk mail function, which learns from your repeated deletion of “get cash fast” emails to send them straight to junk.
Natural Language Processing
Natural language processing applies computational models to text or speech data. Its familiar applications include translation between languages, programs allowing humans to write text by speaking, and extraction programs that transform unstructured text into databases that can be searched and browsed.
Big data refers to data sets so large or complex that traditional data processing techniques are inadequate. Using advanced computer technology, however, it is increasingly possible to capture, curate, analyze, search, share, store, transfer, and visualize huge amounts of data.