Guest post by Jonathan Patterson, DWF Ventures’ managing director and head of development
Patterson: Augmentation, not automation
DWF has embarked on a 30-month knowledge transfer partnership (KTP) with the University of Manchester, with the ultimate aim of transforming how the business uses data and harnessing that data to provide greater value to clients through efficiencies and analytics.
This is the third in a series of quarterly blogs on how the KTP is developing, which explores the achievements of the project to date. Read the first blog here and second here.
Nine months in, one of the achievements has been the way in which the KTP is helping to respond to real-life working situations by creating efficiencies and better ways of working.
“Alexa, how can DWF’s knowledge transfer partnership help me to make simple decisions more quickly?”
Since our partnership with the University of Manchester began, this is one of the biggest questions that has been on the lips of the KTP team at DWF and has resulted in the creation of a specific project to address it.
Whilst the people closest to the KTP have a good idea about how to answer the question, as awareness grows of what we are doing and more and more people become interested, now feels like a good time to start to share some of those perspectives:
David Robinson, operations director: “Our insurance legal file handlers are trained to a level of expertise that enables DWF to be leaders in its field. The project was commissioned because we believe that data analytics can enable support in a practical way that aids both understanding and insight.
“We all have that little voice in our ear that guides us to the right answer – now that voice can be powered by analytics.”
Dr Mayowa Ayodele, KTP associate, data process and applications scientist: “The project started with the motive of helping DWF teams make more informed decisions when handling insurance claims. The most powerful element is the ability to help our teams support one another.
“Lawyers are able to help each other by accurately capturing data relating to their experiences whilst the data-driven assistant uses this data to suggest an optimal strategy.
“As a business, we seek to understand our clients, do things differently and engage our people which this project helps us to achieve.”
Darren Buckley, senior analyst: “Imagine that you were dealing with a case and you could instantly recall every single similar case that DWF has ever dealt with and what the outcomes of all those other cases were.
“Imagine that as well as being able to recall all of that information, you were also able to instantly process thousands of calculations and come up with what is statistically the most likely settlement outcome for your case, whether that’s the amount of time it should take to settle, how much it should settle for, or both. Would that information be useful to you?
“What we’re doing is using data science and technology to give you exactly that information, at the push of a button, leaving you with the simple task of deciding how best to use the case-handling advice.
“Think of it as the most experienced, knowledgeable lawyer there ever was, and that person is now your very own personal mentor.”
Dr Nadia Papamichail, senior lecturer in information and decision systems, Alliance Manchester Business School: “Data analytics and artificial intelligence are viewed as disruptive technologies. Fostering a decision culture by engaging with staff is increasingly important.
“Rather than merely providing a technical solution to a case-handling problem, this project delivers a holistic approach by focusing on lawyers and understanding their needs.
“The purpose of artificial lawyers is to augment legal ability and expertise. Improved productivity, efficiency and accuracy matter, but the ultimate goal is to empower lawyers to undertake more value-added decision tasks.”
To make all of this a reality is a series of challenges, and ones which we continue to work through via the KTP.
The most tangible challenge is the ability to capture the right granular level data without significantly increasing the burden of data capture. Data inconsistency is another problem which, when fixed, will significantly improve our ability to develop statistical models with it.
Whenever people talk about AI or data in law there can be a backdrop of concern that human jobs will be replaced by technology.
One way in which we overcome this challenge is by being clear in the design principles that the focus is supporting the relevant experts to make better, quicker decisions, and not making those decisions for them.
We are definitely talking about augmentation, not complete automation.
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