How do we separate fact from fiction in machine learning and AI - particularly in legal? Our new whitepaper - machine learning in contracts - introduced by Artificial Lawyer, explores myths, realities and possibilities of applying machine learning to contracts.
When Pavel and I co-founded Juro in 2016, the hype about AI in legal tech was really starting to gain traction. While AI has always been part of our vision at Juro, we've seen more and more demand for AI-enabled products as the months go by. Sometimes clients have a clear understanding of the specific use cases for AI technologies, and the specific problems that they aim to solve with them. But we still see - understandably - some confusion among in-house lawyers around when and why investment in AI-enabled platforms makes sense.
The phenomenon of people seeking “legal AI” as an end in itself, rather than looking for a solution to their specific problem, is something that has increased markedly in recent years. There are three obvious reasons: firstly, lawyers are mandated to seek innovation in the way they deliver services (often driven by cost pressure). Second, AI has such incredible potential to change the legal industry forever - and in some ways, it already has. But third, with the possible exception of blockchain, it is perhaps the most hyped legal topic we’ve ever seen.
The combination of these three factors means interest in AI has vastly outpaced the basic understanding of what it is, the implications of AI and why - if at all - it is relevant to the specific legal problems you’re trying to solve.
This whitepaper aims to align those two things. Because the truth is that while the hype may be overblown, the AI deployments in machine learning that we’ve built in Juro could have a profound affect on the pain points that people feel when they manage legal processes - especially in relation to contracts.
And unlike many of the ML applications being used by lawyers, they require much less effort to set up and make useful.
Our core data science team of Dr. Matt Upson, who was the first to ship an ML model into production for the UK Government, and Aleksej Ermolaev, our machine learning engineer, have spent a year exploring, building and deploying machine learning models for contracts and more importantly looking at the problems AI can (and cannot) solve.
In our machine-learning whitepaper, produced in partnership with Artificial Lawyer, Matt explores what’s real and what’s possible, as well as defining all the key terms in machine learning and talking through the practical implications of these breakthroughs for legal. We hope you find it interesting. ✨
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