AI in family law - my submission to the House of Lords' Select Committee

Lords a listening – my submission to the House of Lords’ Select Committee on Artificial Intelligence

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On 11th October 2017, a submission of evidence I made to the House of Lords’ Select Committee on Artificial Intelligence (AI) was published. Sitting alongside a number of other evidence-backed papers considering the economic, ethical and social implications of advances in AI, I wanted to be clear about the impact and opportunity on my field, the provision of family law services.

AI in family law

Here are some key extracts from my answers to the questions posed which are based on the practical experience of Family Law Partners in developing our own technology, first a ‘low-tech’ legal triage platform and then encompassing data capture and analytics to machine learning and AI:

How can the general public best be prepared for more widespread use of artificial intelligence?

By experiencing AI in their everyday lives particularly if AI offers a solution to, or at least the mitigation of, a problem. The problem can be framed for the purposes of this submission as the difficulties encountered by individuals seeking affordable legal advice from suitably trained and qualified family law specialists (normally solicitors, barristers and chartered legal executives). This is an access to justice challenge. In the specific context of family law advice, the challenge has been exacerbated by the withdrawal, with some exceptions, of public funding (Legal Aid) for individuals experiencing family relationship breakdown. The private sector is unable to replace the gaps left by the withdrawal of Legal Aid but has innovated to make privately paid legal advice more affordable.

Of course, most of the public already encounter AI in their daily lives via subscriptions to AI enabled Software as a Service (SaaS) platforms such as Spotify, Netflix and Amazon.

Who in society is gaining the most from the development and use of artificial intelligence and data? Who is gaining the least? How can potential disparities be mitigated?

The question we would pose is: When will the benefits of AI more directly benefit the individual consumers of legal services? Is it acceptable for private individuals in pressing need of legal advice but constrained by affordability to wait patiently for the digital crumbs of innovative AI to fall from the high table of the Magic Circle firms to the level of the high street? Our view is that our clients should not wait.

Family law clients are facing the stress of life-changing events for themselves and their children and we would argue that their need to access affordable legal advice is as great, if not greater, than for corporate entities. If technology, including AI in family law, can offer these efficiencies and savings and make legal services available to private clients of modest means then we should make such development a priority even if that means juggling the limited resources available to an SME.

Client-first thinking

The Committee has invited participants to define AI. We prefer to think of AI as ‘Augmented Intelligence’ rather than ‘Artificial Intelligence’. Family law clients prefer to see their advisers in the flesh. They want access to humans who can offer empathy, guidance and understanding: what some call soft skills or EI (emotional intelligence). In the case of lawyers, and family law specialists in particular, the EI should be complementing the years of professional training and the highest standards of professional integrity overseen by a vigilant regulator.

Couched in these terms, we could say that access to such human interaction – a face to face delivery – is the gold standard. If we accept that is the standard we should adopt then we should explore how the highly-trained family lawyer possessed of EI should access augmented intelligence, to reduce risk, automate otherwise laborious, expensive processes and make it easier for private clients of modest means to access this gold standard.

The full paper can be accessed here:

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