A highly regulated and historically conservative industry, the law’s relationship with technology is best described as complicated. In this era of innovation and transformation, digital technologies have revolutionised almost every traditional business model.
The law won’t prove to be the exception to the rule. The sector is facing an influx of artificial intelligence (AI) and automation solutions. “Lawtech” start-ups are tackling everything from practice management to contracts, risk and compliance to analytics and search.
Following our work in the sector, what do I consider to be the emerging risks of applying AI technologies within the legal sector and how will the sector handle them in the next 12 months?;
As with other industries that handle highly-detailed, highly-sensitive personal data, regulators want to ensure that it is used legally and ethically. They want clarity from AI tools, especially when it comes to decision-making processes, to ensure that it’s free of bias.
The problem comes when firms start using deep learning and neural networks. This is because the application of this subset of AI can result in “black box” decision-making, where software or systems operate in such a way that it’s hard to know for certain the internal logic used to reach a decision. It’s an issue most commonly encountered when using deep learning or neural networks to process large quantities of data.
In an industry such as law, in which data is so detailed and so vast, transparency in decision-making is difficult to obtain. When deep learning or neural networks are deployed at speed and scale, the algorithms become big, complex formulas — and it becomes impossible to identify and assess the millions of micro-decisions and complex connections that inform the output.
So, my view is that we’re likely to see automation continue to lead the way when it comes to the adoption of AI within the industry, with firms using the technology to streamline operations. AI will help firms match internal process with external regulation, extract data to serve legal notices and free-up junior lawyers from repetitive tasks, so that they can focus on higher-value issues. This capacity to transform back-office processes and improve efficiency is highly-valued in an industry dominated by case law, precedent and research. However, the application of deep learning and neural networks still has some way to go and at present, the technology is simply not able to deliver transparency in a way that will satisfy external regulation and legislation.
Ben Taylor, Chief Executive Officer, Rainbird
