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Rethinking the Legal Profession in the Age of ML

By now, Machine Learning is soundly in the public domain as its wide impact is being felt across many industries around the world as they go through digital transformations. Although the spearheading ML applications have come from the usual suspects such as Internet companies and software firms, the waves of automation and data-driven decision making have been recently crushing on the shores of the Legal Services industry (article in Spanish).

A typical law firm in the Western world employs tens or even hundreds of attorneys specializing in different practice areas e.g., intellectual property, corporate, civil, criminal, constitutional law. The business of legal services remains perhaps the very definition of a human-driven industry essentially relying on increasing the employee count to be able to scale to higher revenues. Such growth no doubt may present some efficiencies, but there’s no evidence of strong network effects letting few players dominate the market. So it becomes even more important to make the best use of your expensive human resources to succeed in this highly fragmented industry full of niche players.

Whatsmore, the legal profession is historically known as quite conservative in its business practices since it is educated on precedent and is less forgiving towards experimentation and failure. However, a combination of factors sweeping the industry is pushing more firms to reconsider this stance. For starters, clients are demanding faster, more intuitive and accessible legal advice delivered over multiple channels and geographies. In addition, billable hours for less sophisticated commodity aspects such as research or project management are being scrutinized more closely as opposed to reasoning and judgment.

In their 2018 predictions, the Legal Institute For Forward Thinking outlines that AI will be a ticket for admission as a driver of consistent, high-quality client experience. This suggests leading law firms will have to be run more like other companies with an emphasis on operational efficiency. Those who are left behind will have to do with less profitable clients and a shrinking client base.

How can ML make a difference?

Digitalization is the norm in today’s business environment, which means detailed data on legal evidence, contracts, legislation, and jurisprudence are all available in easily accessible digital formats. However, the bigger challenge remains in making sense of this data deluge, which where most law firms have been struggling to keep up with. Unsurprisingly, a lot of them are turning to technology to be able to deal with it without having to multiply human experts on their payroll.

The typical legal practice tasks involve reviewing and generating documents, discovering useful associations and understanding motivation and behavior of the parties involved in a legal dispute. State of the art Machine Learning techniques that work with unstructured data have a high degree of applicability in these tasks, in turn, reducing the burden of excessive paperwork. For example, contract specifics like parties involved, payment terms, or start and end dates can be automatically extracted and mapped for faster due diligence or anomaly detection.

On the other hand, legal firms share similar administrative challenges as many other firms like human resources management, pricing, forecasting or customer relationship management. By some accounts, over 50% of partner and associate time is being spent on such administrative tasks. The more efficient these peripheral activities and their underlying processes run, the more profitable the firm becomes as it leaves more resources to be creatively deployed towards new specializations and differentiated service offerings. As you may have come to suspect, with a little human expert help, Machine Learning can connect many of these dots better than humans alone can.

These opportunities do not merely represent forward-looking statements and wishful thinking either. As the old adage goes: the future is here, it’s just unevenly distributed. In fact, we’re already witnessing ML being successfully introduced into more sub-domains of law with use cases ranging from automated jurisprudence aids and predicting judicial decisions to predicting the success of claims. In all three examples, AI systems did as good if not better than collections of human experts. Are these the Google Deepmind moments of the legal industry? Time will tell.

Predictive Apps on BigML

As for BigML, thanks to our engagement with a leading North American law firm, we have been able to implement a solution to help predict (in detail) future legal services demand, associated resource requirements and optimal pricing for new matters by analyzing more than a decade’s worth of invoices and other expense reports. The resulting system provided partners and administrators unprecedented insights into cost drivers by matter type, jurisdiction, litigation team structure, and other case-specific factors. None of this could be replicated even by the most experienced members of the firm.

Other BigML customers in the legal space also keep adding to the creative ways ML innovations are deployed in the legal industry. For instance, NDA Lynn recently launched its automated NDA checker service, to begin with, training their models on hundreds and then thousands of variations of Non-disclosure Agreements. This collection of data produced interesting patterns that can serve as early warning signs for NDA Lynn customers looking to address any undue risks before agreeing to the terms stated in their NDA.

This simple, narrow-AI example will likely find its way to many other types of contracts over time as digital data samples increase in size and the need to manage risks in a quantifiable way mounts in today’s ultra-competitive legal marketplace. As such, leading-edge law firms see the need to add many more ML-powered micro-services capabilities to their next generation IT platforms making lawyering more efficient, accurate, and less labor intensive. If this trend stays in place, CTO or CDO jobs in law firms may be a hotter commodity than they’ve been perceived so far by top-notch technical professionals, further attracting the best and brightest young lawyers feeling right at home working with ML-driven systems.

Should be a fun ride to see how it all unfolds and whether one of the oldest industries can pass its test against technology!


About the author:

Atakan Cetinsoy is Vice President - Predictive Applications at BigML Founded in january 2011 BigML is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modeling tasks. BigML is helping thousands of analysts, software developers, and scientists around the world to solve Machine Learning tasks "end-to-end", seamlessly transforming data into actionable models that are used as remote services or, locally, embedded into applications to make predictions.

This article was originally published as a post on March 20, 2018 on the webiste of BigML. Other posts from Atakan Cetinsoy can be found here.

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