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The recent surge in popularity of artificial intelligence as a workplace tool has become quite the hot button issue lately—namely, is AI going to render large portions of the legal industry obsolete?
The rise in automation of routine tasks—such as drafting documents—tends to put us on pins and needles, as it forces us to reflect on how mundane or easily replicated our skill sets can be, particularly by machine learning.
To-wit, the looming fear is that artificial intelligence will result in the outsourcing and outbidding of legal work currently performed by litigators.
While it is still wise to give side eye and skepticism to artificial intelligence in the legal field at large, there is also much to be gained from the use and integration of artificial intelligence in the legal industry—particularly in dispute resolution, where the success of negotiation relies upon the reasonable expectations of both parties who come to the table.
Without delving into the nerdy details of how and why artificial intelligence capabilities have expanded and changed in recent years, suffice it to say that artificial intelligence as a technological device has broadened (and it’s not done cooking yet) into a large spectrum of tools and applications, with the primary objective of reducing time spent on mechanical tasks.
One category of artificial intelligence—unimaginatively dubbed “assistive technologies”—is rapidly becoming more prevalent in legal analyses. Assistive technologies consume enormous datasets quickly, analyze, interpret and produce predictions or summaries of relevant outcomes based upon the data.
Essentially, assistive technologies can absorb, break down, and apply the essence of different data sets to quickly and accurately provide a range of information useful to a specific situation without undertaking the headache of sorting through irrelevant or outdated information—not to mention at an exceptionally higher rate of speed than possible for the average human brain.
As such, mediators who begin using assistive technology can eliminate time spent reviewing and analyzing data at a human-pace—such as those pesky confidential mediation statements, medical chronologies and medical expense charts and summaries.
If parties opt to submit pleadings, transcripts, photographs, videos, or discovery responses in support of their mediation statement, the assistive technology sift through the information to identify key patterns and highlight anomalies.
In mediation settings, assistive technologies have also been used to analyze previous settlements and disputes, apply core common features, and provide predictions of future similar disputes. It is important to add though, the results and summaries produced by assistive technologies are only as reliable and accurate as the data set used to develop the results.
While there will always be a risk of erroneous or outdated information funneled into these data sets, the more frequently AI is used to analyze and predict settlements, the more accurate it will become over time, as it would continually be fed updated data and mediation results to tailor its predictions in the future.
“If AI software is faster, better, and more accurate at analyzing datasets in order to predict reasonable settlements, why are third party humans still necessary?” you might be asking yourself.
Summaries and predictions based on large data sets are meaningless if the information is not presented at the right point in time using the right tone. While AI is most effectively used to boil down conflicts to statistical likelihoods, AI does not have emotional intelligence, or the capacity to develop emotional intelligence, that is used by mediators on a daily basis.
The value of a summary of alleged injuries, treatment, and statistically-accurate settlement ranges for similar issues in similar settings/jurisdictions is only as strong as the method of delivery of that information. Simply placing the information in front of a party during mediation could result in zero sway to that party’s expectations and attitude in mediation.
When a mediator is involved, the parties’ demeanors, attitudes, and non-verbal social cues are all being assessed and interpreted using the mediator’s own personal experiences. Mediators frequently assess the temperature in the room and aim to provide potentially upsetting information at key moments in time.
In the same vein, mediators are often used by aggrieved parties to vent their frustrations with mediation, the opposing party, or the entire litigation process in general. Many mediators understand that more than anything else- people who attend mediation tend to feel wronged in some way, and in taking the step of validating their feelings, can greatly impact the trajectory and likelihood of settlement between the parties.
That is a figure that cannot be translated into a mathematical dataset, at least not yet.
The variety of benefits and drawbacks that artificial intelligence could have in the legal field, particularly including mediation, remains unseen.
There are a host of potential ethical issues and data source problems since many settlements are confidential, which could limit the data pool being drawn upon by AI. However, until we actively begin adapting to the use of AI technology in some capacity, we will not be able to meaningfully debate or assess what rules and parameters should be set in place for future practice with AI.
We need rules and structure moving forward, but first, we need to dip our large toe in the water to test the temperatures first.
While we should treat AI with precaution and be mindful of its pitfalls, and while we might not like the concept of AI as taking over parts of our jobs, it could be useful in enhancing skill sets in other areas to boost productivity in other ways. As Mick Jagger has reminded me on many a dull drive on rural county roads, “You can’t always get what you want, but if you try sometimes, well you just might find, you get what you need.”•
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Carly Roseboom is an attorney at Kightlinger & Gray LLP. Opinions expressed are those of the author.
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