Harriet Hunter, law student at the University of Central Lancashire, explores the implications of AI in the development of weaponry and its effect on armed conflict in international humanitarian law

Artificial Intelligence (AI) is arguably the most rapidly emerging form of technology in modern society. Almost every sector and societal process has been or will be influenced by artificially intelligent technologies— and the military is no exception. AI has firmly earned its place as one of the most sought-after technologies available for countries to utilise in armed conflict, with many pushing to test the limits of autonomous weapons. The mainstream media has circulated many news articles on ‘killer robots’, and the potential risks to humanity — however the reality of the impact of AI on the use of military-grade weaponry is not so transparent.

International humanitarian law (IHL) has been watching from the sidelines since the use of antipersonnel autonomous mines back in the 1940s, closely monitoring each country’s advances in technology and responding to the aftereffects of usage.

IHL exists to protect civilians not involved directly in conflict, and to restrict and control aspects of warfare. However, autonomous weapons systems are developing faster than the law  — and many legal critics are concerned that humanity might suffer at the hands of a few. But, in a politically bound marketplace, is there any place for such laws, and if they were to be implemented, what would they look like, and who would be held accountable?

Autonomous weapons and AI – a killer combination?

Autonomous weapons have been a forefront in military technology since the 1900’s – playing a large part in major conflicts such as the Gulf War. Most notably, the first usage of autonomous weapons was in the form of anti-personnel autonomous mines. Anti-personnel autonomous mines are set off by sensors – with no operator involvement in who is killed;  inevitably causing significant loss of civilian life. This led to anti-personnel autonomous mines being banned under the Ottawa treaty 1997. However, autonomous weapon usage had only just begun.

In the 1970’s autonomous submarines were developed and used by the US navy, a technology which was subsequently sold to multiple other technologically advanced countries. Since the deployment of more advanced AI, the level of weapons that countries have been able to develop has led to a new term being coined: ‘LAWS’. Lethal Autonomous Weapons Systems (LAWS)  are weapons which use advanced AI technologies to identify targets and deploy with little to no human involvement.

LAWS are, in academic research, split into three ‘levels of autonomy’ – each characterised by the amount of operator involvement that is required in their deployment. The first level is ‘supervised autonomous weapons’ otherwise known as ‘human on the loop’ — these weapons allow human intervention to terminate engagement. The second level is ‘semi-autonomous weapons’ or ‘human in the loop’, weapons that once engaged will enact pre-set targets. The third level is ‘fully autonomous weapons’ or ‘human out of the loop’, where weapons systems have no operator involvement whatsoever.

LAWS rely on advances in AI to become more accurate. Currently, there are multiple LAWS either in use or in development, including:

• The Uran 9 Tank, developed by Russia, which can identify targets and deploy without any operator involvement.
• The Taranis unmanned combat air vehicle being developed in the UK by BAE Systems, an unmanned jet which uses AI programmes to attack and destroy large areas of land with very minimal programming

The deployment of AI within the military has been far reaching. However, like these autonomous weapons, artificial intelligence is increasingly complex, and its application within military technologies is no different. Certain aspects of AI have been utilised more than others. For example, facial recognition can be used on a large scale to identify targets within a crowd. Alongside that, certain weapons have technologies that can calculate the chances of hitting a target, and of hitting a target the second time by tracking movements — which has been utilised in drone usage especially to track targets when they are moving from building to building.

International humanitarian law — the silent bystander?

IHL is the body of law which applies during an armed conflict. It has a high extra-territorial extent and aims to protect those not involved in the practice of conflict, as well as to restrict warfare and military tactics. IHL has four basic tenets; ensuring the distinction between civilian and military, proportionality (ensuring that any military advances are balanced between civilian life and military gain), ensuring precautions in attack are followed, and the principle of ‘humanity’. IHL closely monitors the progress of the weapons that countries are beginning to use and develop, and are (in theory) considering how the use of these weapons fits within their principles. However, currently the law surrounding LAWS is vague. With the rise of LAWS, IHL is having to adapt and tighten restrictions surrounding certain systems.

One of its main concerns surrounds the rules of distinction. It has been argued that weapons which are semi, or fully autonomous (human in the loop, and out of the loop systems) are unable to distinguish between civilian and military bodies. This would mean that innocent lives could be taken at the mistake of an autonomous system. As mentioned previously, autonomous weapons are not a new concept, and subsequent to the use of antipersonnel autonomous mines in the 1900s,  they were restricted due to the fact that there was no distinction between civilians ‘stepping onto the mines’, and military personnel ‘stepping onto the mines’. IHL used the rule of distinction to propose a ban which was signed by 128 nations in the Ottawa Treaty 1997.

The Marten’s clause, a clause of the Geneva Convention, aims to control the ‘anything not explicitly regulated is unregulated’ concept. IHL is required to control the development, and to a certain extent pre-empt the development of weapons which directly violate certain aspects of law. An example of this would be the banning of ‘laser blinding’ autonomous weapons in 1990 — this was due to the ‘laser blinding’ being seen as a form of torture which directly violates a protected human right; the right to not be tortured.  At the time, ‘laser blinding’ weapons were not in use in armed conflict, however issues surrounding the ethical implications of these weapons on prisoners of war was a concern to IHL.

But is there a fair, legal solution?

Unfortunately, the chances are slim. More economically developed countries can purchase and navigate the political waters of the lethal autonomous weapons systems market — whilst less economically developed countries are unable to purchase these technologies.

An international ban on all LAWSs has been called for, with legal critics stating that IHL is unable to fulfil its aims to the highest standard by allowing the existence, development and usage of LAWS. It is argued that the main issue which intertwines AI, LAWS and IHL, is the question – should machines be trusted to make life or death decisions?

Even with advanced facial recognition technology — critics are calling for a ban, as no technology is without its flaws — therefore how can we assume systems such as facial recognition are fully accurate? The use of fully autonomous (human out of the loop) weapons, where a human cannot at any point override the technology – means that civilians are at risk. It is argued that this completely breaches the principles of IHL.

Some legal scholars have argued that the usage of LAWS should be down to social policy — a ‘pre-emptive governing’ of countries who use LAWS. This proposed system allows and assists IHL in regulation of weapons at the development stage – which, it is argued, is ‘critical’ to avoiding a ‘fallout of LAWS’ and preventing humanitarian crisis. This policy would hold developers to account prior to any warfare. However, it could be argued that this is out of the jurisdiction of IHL which is only applied once conflict has begun — this leads to the larger debate of what the jurisdiction of IHL is, in comparison to what it should be.

Perhaps IHL is prolonging the implementation of potentially life-saving laws due to powerful countries asserting their influence in decision making; these powerful countries have the influence to block changing in international law where the ‘best interests’ of humanity do not align with their own military advances.

Such countries, like the UK, are taking a ‘pro-innovation’ approach to AI in weaponry. This means that they are generally opposed to restrictions which could halt progress in the making. However, it has been rightly noted that these ‘advanced technologies’ under the control of terrorist organisations (who would not be bound to follow IHL) would have disastrous consequences. They argue that a complete ban on LAWS could lead to more violence than without.

Ultimately…

AI is advancing, and with this, autonomous weapons systems are too. Weapons are becoming more advantageous to the military – with technology becoming more accurate and more precise. International humanitarian law, continually influenced by political stances and economic benefit to countries, is slowly attempting to build and structure horizontal legislation. However, the pace at which law and technology are both developing is not comparative and concerns many legal critics. The question remains, is the law attempting to slow an inevitable victory?

Harriet Hunter is a first year LLB (Hons) student at the University of Central Lancashire, who has a keen interest in criminal law, and laws surrounding technology; particularly AI.

Université Toulouse Capitole LLM student Sean Doig examines technology’s impact on legal education and training

No profession is immune to the intrusion of disruptive technologies. Inevitably, the legal profession is no exception, and the practice of law and the administration of justice has grown incredibly reliant on technology.

The integration of new legal technologies into legal services is driven by the incentive to provide more efficient, cost effective, and accessible services to its clients. Indeed, modern lawyers are implementing paperless offices and “cloud-based practice-management systems, starting up virtual law practices, and fending off challenges from document preparation services like Legal Zoom.”

Such profound change has even shaped new specialisms within the legal profession, including those known as ‘legal technologists’; a group of skilled individuals who can “bridge the gap between law and technology.” While the name suggests connotations of a ‘legally-minded coder’, the reality is that the majority of professional legal technologists lack any training or experience in both the practice of law and in the profession of engineering and technology management.

Legal technologists is a lucrative and growing niche, and it is insufficient for those professionals to lack the experience and knowledge in the practice of law if they are to develop sustainable legal technologies to assist the delivery of services to clients.

Indeed, disruptive technologies are constantly evolving, and with the rapid advancement of Artificial Intelligence (‘AI’) and the Metaverse, there is a need for immediate change as to the training of the next generations of legal minds. While this sort of fearmongering around obsolete skills and doomed professions is relatively commonplace among CEOs of AI companies, the need for upskilling and adaptability of lawyers has been reiterated by skeptical academics and legal professionals for years.

As early as the 1950’s, diction machines and typewriters changed the working practices of lawyers and legal secretaries. In the 1970’s, law firms began using computers and LexisNexis, an online information service, which changed the way legal teams performed research to prepare their cases. One of the more well-known ‘doomsayers’, Richard Susskind, whose book boldy — although perhaps rather prematurely – titled The End of Lawyers was published in 2008 — well before the era of ‘Suits’!

Despite Susskind’s earlier predictions of impending doom of the end of lawyers, the author’s subsequent book, Tommorrow’s Lawyers, surpasses the ordinary opinion that technology will remove jobs; instead, opts that technology will assist the work of professionals and more jobs will involve applying technological solutions to produce a cost-efficient outcome. Although technology is developing rapidly to assist professionals, Susskind identifies that there is a lack of enthusiasm among law firms to evolve their traditional practices. Conversely, the enthusiasm of law firms to incorporate technology is normally where AI or other technologies are able to boost profits and lower operating costs, rather than assisting the lawyer and delivering for the client.

The incentive for law firms to incorporate technology into their working practices is purely economical and fear oriented. Firms that do not incorporate technology will lose clients to those competitors that have efficient technological means at their disposal. There is little credible advice as to how firms can affectively alter their business model to integrate technology. After all, the billable hour is the crux of a law firm, and with AI speeding up historically slow and tedious work, its value is diminishing.

Without dwelling too much on the fundamentals of capitalism and its effectiveness as an economic system, it is important to note that technology companies — such as OpenAI and Meta – are mostly funded and motivated by shareholders. The rapid nature in the development of technology is to produce results and dividends for those shareholders. In order for the product to perform well economically, there is a rush to outdo competitors and to be disruptive in the market. If successful, the value of the company will increase, the value of the shares will increase, and the more equity the company will have to continue to grow.

This means that technology is advancing at a fast rate and is outpacing the technical skills of professionals. The cost of new technologies factors in the markup that tech companies seek to satisfy their shareholders and advance their research & development (R&D). As Susskind notes, the durability of small law firms will be put into question in the 2020’s against the rise of major commercial law firms that are able to afford to invest in competitive, new technologies.

What does this mean for law students? New skills are required to enter the new technological workforce, and those graduates that meet the skillset will be more in demand than the rest of their cohort. As a result, legal education must equally evolve to adequately prepare law students for working in technological law firms. As Susskind highlights: “law is taught as it was in the 1970’s by professors who have little insight into or interest in the changing legal marketplace”, and graduates are ill-prepared for the technological legal work that their employer is expecting from them.

It should be noted that some graduate and post-graduate courses do exist to facilitate the teaching of some of the technological skills to prepare individuals for the new workplace. Indeed, for example, there is a simulation currently in use in a postgraduate professional course called the Diploma in Legal Practice at the Glasgow Graduate School of Law. Nevertheless, the idea here is that the burden should be placed on law schools and that technological skills should be taught at the earliest stage in order to best prepare graduates for the workplace of tomorrow.

Although it is argued that the original purpose of the LLB is to teach black letter law and the skills for legal practice should be left for post-graduate legal training, this neglects those law students who do not wish to pursue the traditional post-graduate legal education; rather opting for an alternative career path in law.

In order for the value of an LLB to be upheld, it must adapt to meet the growing demand of the industry it serves. Its sanctity and popularity rests on its ability to be of use to any student seeking to have the best possible skills and, therefore, prospects in the job market. If the LLB is to survive, itself must compete with more attractive courses such as ‘Computer Science’, ‘Data Analysis’, and ‘Engineering’. It is not enough for law professors to continue to falsely assume that “students already get it”, or that if graduates work for a law firm then critical technology choices have been determined, “including case management software, research databases, website design, and policies on client communication.”

Furthermore, firms are “increasingly unwilling to provide training to incoming associates” and seek those graduates who already possess background knowledge. Undoubtedly, technology skills will elevate students’ employability, and those with tech skills will be in high demand by traditional law firms and by tech companies that service the legal industry.

While some law schools have been introducing “Legal Technology” or “Law and Technology” modules into their curriculums, it can be argued that they are insufficient to cover the array of specific skills that need to be taught, and are rather focusing merely on the impact of technology in the legal sector. The lack of innovation in law schools is placed on the lack of imagination on the part of law professors and its institutions; fearful of experimenting with the status quo of syllabises. Institutions with the courage to experiment with their curriculum to teach desirable skills in the legal market will attract and better serve a greater number of students for the new world of work.

Perhaps the most elaborate attempt to revolutionise legal education is the theoretical establishment of an MIT School of Law by author Daniel Katz. ‘MIT Law’ would be an institution that delivered a polytechnic legal education; focusing on “the intersection of substantive law, process engineering, computer science and artificial intelligence, design thinking, analytics, and entrepreneurship.” The institution would produce a new kind of lawyer; one that possessed the necessary skills to thrive in legal practice in the 21st century. With science, technology, engineering, and mathematics (“STEM”) jobs dominating the job market, there is an overlap into the legal market; giving rise to a prerequisite or functional necessity for lawyers to have technical expertise to solve traditional legal problems that are interwoven with developments in science and technology.

This hypothetical law school may seem far-fetched, but the underlining principle should be adapted to the modern LLB. Indeed, the curriculum should choose its courses upon the evaluation of the future market for legal services and adapt to the disruptive technologies becoming commonplace in the workplace. A hybrid of traditional law courses such as contract law, with more technical courses such as Machine Learning or E-Discovery should become the new normal to ensure the effective delivery of the best LLB of the future. Each course would be carefully evaluated in light of the current and future legal labour market to ensure that students are given the best possible chances after leaving the institution; whether they go on to post-graduate legal studies or not.

Sean Doig is an LLM student at Université Toulouse Capitole specialising in International Economic Law. He is currently working on his master’s thesis, and displays a particular interest in international law, technology and dispute resolution.

LSB writes to Lord Chancellor and tech minister

The legal profession’s super regulator, the Legal Services Board (LSB), is planning to review its guidance on AI training for lawyers, a newly published letter reveals.

The letter, authored by Richard Orpin, interim chief executive of the LSB, is directed to Technology Minister Michell Donelan MP and Lord Chancellor Alex Chalk MP, in response to a request from the Department for Science, Innovation, and Technology.

“We recognise the increasingly important role that technology, including AI, plays in society and its potential to improve the diversity and reach of legal services,” Orpin writes. He goes on to note that “greater reliance on AI in the production of legal advice has the potential to introduce additional risks to consumers of legal services”.

The letter continues by stating that the LSB is planning to undertake a review of its existing guidance on regulatory arrangements for education and training.

The letter continues:

“This review is likely to include the consideration of how regulatory frameworks for education and training should reflect what requirements are necessary to ensure that legal professionals are competent in the use of technology, such as AI tools, at both the point of entry into the profession and throughout their careers.”

The letter also notes the role of the individual regulators in managing the future of AI usage. Whilst the existing guidance does not “specifically require” individual regulators to provide their own AI guidance, it does state that “individual regulators are best placed to assess these risks within their regulated communities and put in place mitigation strategies to address them in line with the outcomes in our guidance”.

The Solicitors Regulation Authority published its own guidance back in November last year looking both at the uses of AI in law, and the wide range of potential risks.

Ahead of his appearance at LegalEdCon 2024 next month, LexisNexis’ Matthew Leopold discusses its latest AI offering and how it will likely impact the legal industry

“My specialism is to take a brand and challenge people’s assumptions about it,” says Matthew Leopold, Head of Brand and Insight at LexisNexis UK. “My job is to get people to feel more positive about a brand and engage with it in a different way.” Having built a specialism in brand management at tech companies, Leopold notes ironically that one of the biggest challenges of his role at LexisNexis is adapting a brand that almost every lawyer already knows. “Not only is it well known, but LexisNexis is a brand which lawyers understand and trust, and that’s because of the wealth of legal content that underpins the technology.”

Having evolved from a single database created by John Horty in 1956, LexisNexis has moved away from traditional publishing by becoming a global player in legal technology. Leopold is keen to stress the company’s tech-y credentials. “We ultimately create solutions that provide the right legal content at the right time; our technology helps people to find the diamond in the haystack of content,” says Leopold who will be speaking at LegalEdCon 2024 in London on 16 May. On the rewards of his role, he says, “it’s really interesting to be able to create cutting edge legal technology with this underlying, incredibly valuable, exceptionally well-trusted legal content.”

In response to developments in generative AI technology, LexisNexis developed and launched its own AI tool, Lexis+AI, at the end of 2023. Available in the UK in the coming weeks, Legal Cheek Careers was keen to ask Leopold for the key features of this new tech. “At launch, there are four main features that Lexis+AI is going to offer,” he tells us. “First is the conversational search feature. Imagine that you have a really knowledgeable associate sat on the desk next to you, and you can ask them a legal question and get a legal answer in response, pointing you in the direction of all the relevant information.” He continues, “the purpose of the conversational aspect of the search, means you can clarify, and ask a follow-up question to which Lexis+AI responds to your requests and refines answers.”

Explaining the benefits of this feature in terms of access to legal research, Leopold explains that, “the sorts of conversations that you would usually have with a human, you can now have with AI — and in this context, it allows you to really mine the depths of the law.” Grounded on the already expansive LexisNexis legal database, Lexis+AI can link you directly to relevant precedents, case law and practice notes within seconds. Leopold explains that this is key to reducing AI ‘hallucinations’ — circumstances where AI models produce nonsensical, falsified information. “We can minimise hallucinations as much as possible,” he says, “however linking directly to the content means that lawyers and students can quickly evaluate AI answers with their own eyes”.

The second key feature of this potentially industry-altering tech is its summarisation capabilities. Leopold notes that, “public access AI tools, such as ChatGPT  are not legally trained. They don’t understand the legal use-case for what it’s doing.” The difference with Lexis+AI is that “rather than producing a summary of a case, it presents a case digest which includes jurisdiction, key material facts, controlling law and more.”

Lexis+AI also boasts drafting capabilities and the ability to upload your own documents for review. “Lexis+AI can help you draft clauses, form arguments, and create letters to clients,” Leopold explains. By integrating the features of the technology, both lawyers and students are able to extract information through conversational search. They can then prompt Lexis+AI to use this information to create legal arguments or letters. “It’s important to emphasise that this will result in a first draft,” Leopold stresses. “We do not proclaim that this is going to be the end result. You would always expect a senior to review the work of junior before it goes to a client. The same is true with AI-generated content.”

In that vein, we ask Leopold how he envisions the future of the legal industry with the introduction of generative AI, and where the boundaries between the lawyers and computers really lie. “AI is the next big frontier,” he says. “There is no avoiding it; it’s a matter of when not if. There are going to be fundamental changes to the legal market., Take the good old billable hour.  It is going to change. In a world where technology can do the heavy lifting of legal research in couple of seconds, the whole idea of charging by the hour becomes difficult to justify.” He predicts that we’re likely going to see an evolution towards value-based pricing in law firms, and more innovative fee structures, as firms transform with the implementation of AI.

“Historically, the legal industry has been a slow adopter of technology,” says Leopold, “This is the first piece of technology that is truly challenging the status quo. Law firms are now considering what this means for their core business and the skills that the lawyers of tomorrow will require. There is a very exciting and busy future ahead for lawyers and the whole legal industry.”

Following the idea that AI is paving the way for some dramatic shifts in the legal industry, we’re keen to hear Leopold’s thoughts on the differences between the role of a lawyer and the role of AI in legal research. “I think that both are the future, and that one can’t really exist without the other,” he says. “We are very clear that Lexis+AI is not created to replace a lawyer. Lawyers need to still be in the loop because they can identify legal context, and other concepts which cannot be trained into an AI model.” Similar issues are raised, Leopold continues, when one considers the human aspect of legal work, requiring negotiation skills, teamwork and often empathy. Ultimately, AI’s ability to reduce manual, administrative legal tasks is huge, leaving lawyers to focus on problem solving, according to Leopold. “AI will do the heavy lifting so that the lawyer can do the heavy thinking.”

Matthew Leopold, Head of Brand and Insight at LexisNexis UK, will be speaking at LegalEdCon 2024, Legal Cheek’s annual future of legal education and training conference, which takes place in-person on Thursday 16 May at Kings Place, London. Final release tickets for the Conference can be purchased here.

Follow @LexisNexisUK

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‘Lawrence’ scored 74% on mock test

The London law firm that created an ‘AI paralegal’ capable of passing part one of the Solicitors Qualifying Exam (SQE) has received £9.5 million of investment from Google.

Lawhive, founded in 2021, hit headlines last November when it’s AI-powered paralegal successfully completed SQE1, scoring 74% on the multiple choice sample questions available on the Solicitors Regulation Authority’s website.

The bot, dubbed ‘Lawrence’, successfully answered 67 of the 90 multiple choice sample questions, despite struggling with what the firm said were issues with “complex chains of logic and wider context”.

Jump forward a few months and Google Ventures, the venture capital arm of Google’s parent company Alphabet, has pumped £9.5 million into the firm.

Commenting on the the need for AI in law, chief executive of Lawhive, Pierre Proner, said:

“The consumer legal market is totally broken and hasn’t really had an update in hundreds of years. It came out of personal experiences of really battling an airline while trying to get money back during Covid, and feeling totally cut out of the legal system. We went to some high street firms to see if they could help and it was far more expensive than was justified to pay.”

Lawrence, he says, keeps lawyers away from “repetitive legal work”, helping clients find cheaper solutions, whilst ensuring that they’re “not getting an AI chatbot, they are getting a real human lawyer working with them”.

On the new investment, Vidu Shanmugarajah, a partner at Google Ventures, said: “Lawhive not only dramatically improves legal workflows but also makes high quality legal advice more accessible and affordable to a broader audience.”

Earlier this month, one of the UK’s top judges, Lord Justice Birss, noted how “AI used properly has the potential to enhance the work of lawyers and judges enormously”. “It will democratise legal help for unrepresented people” he said, adding that “it can and should be a force for good.”

Potential framework for UK to follow

The European Parliament has endorsed a new legal framework concerning the use of artificial intelligence (AI), creating a potential pathway for the UK to follow.

After MEPs voted overwhelmingly in favour of the new laws earlier this week, 523 to 46, certain AI uses look set to be banned, with permitted tech to come under varying levels of scrutiny.

“All AI systems considered a clear threat to the safety, livelihoods and rights of people will be banned, from social scoring by governments to toys using voice assistance that encourages dangerous behaviour,” an article on the European Commission’s site reads.

For those programmes and uses that fall below this level, the legislation plans to divide AI into three tiers; high risk, limited risk, and minimal risk.

The high risk category includes AI used in critical infrastructure, education, safety components, employment processes, law enforcement, migration, and the administration of justice. For AI to be permitted in these areas it must first meet a number of obligations focussed on risk assessment and mitigation, security, and human oversight.

Limited risk focuses on the dangers associated with a lack of transparency in AI usage. These uses will again be subject to obligations, albeit less stringent and focussed on ensuring that people are aware of the use of AI, for example with the labelling of AI chatbots and AI generated text, video, and images.

Those uses considered minimal risk will be unaffected. This category, the Commission says, covers “the vast majority of AI systems currently used in the EU”, and includes AI video games and spam filters.

This comprehensive framework would be the first of its kind, and could provide a pathway for the UK to follow.

Earlier this week the Solicitors Regulation Authority issued a warning to lawyers over the potential fabrication of deepfake clients using AI. This followed previous guidance highlighting the benefits and risks of AI given to solicitors, barristers, and judges over recent months.

Concerns over money laundering and terrorist financing

The Solicitors Regulation Authority (SRA) has issued a new warning about the risk posed by artificial intelligence (AI) to the legal profession in the form of ‘deepfake’ technology.

As part of their regular risk assessments for anti-money laundering and terrorist financing, the SRA has highlighted the potential risks of deepfake technology alongside other emerging and existing issues.

“Not meeting a client face-to-face can increase the risk of identity fraud and without suitable mitigation such as robust identity verification may help facilitate anonymity,” the warning states.

Whilst “not meeting face-to-face may make sense in the context of a given transaction or wider context… where clients appear unnecessarily reluctant or evasive about meeting in person, you should consider whether this is a cause for concern.”

Firms are also told to be aware of the use of AI to create so-called ‘deepfakes’, which can impersonate a real person’s appearance convincingly.

“This increases the risk of relying on video calls to identify and verify your client. If you only meet clients remotely, you should understand whether your electronic due diligence protects you against this, or to explore software solutions to assist in detecting deepfakes,” the SRA adds.

In a speech last week the second most senior judge in England and Wales, Sir Geoffrey Vos, highlighted the continued growth of AI in the legal profession, and its potential for further expansion.

“One may ask rhetorically whether lawyers and others in a range of professional services will be able to show that they have used reasonable skill, care and diligence to protect their clients’ interests if they fail to use available AI programmes that would be better, quicker and cheaper,” Los said.

Noting also the potential use of tech in judicial decisions, he added:

“I will leave over the question of whether AI is likely to used for any kind of judicial decision-making. All I would say is that, when automated decision-making is being used in many other fields, it may not be long before parties will be asking why routine decisions cannot be made more quickly, and subject to a right of appeal to a human judge, by a machine. We shall see.”

Last month Shoosmiths became one of the first law firms to offer guidance to students on the use of AI when making training contract and vacation scheme applications.

Legal Cheek flash poll

One in five students say they have used artificial intelligence (AI) to assist with their training contract, vacation scheme and pupillage applications.

A recent Legal Cheek poll asked students on LinkedIn whether they had used AI platforms such as ChatGPT to help write applications for their dream legal roles. Of the 1,303 who responded, 268 (21%) confirmed that they had done so.

Last week Shoosmiths became one of the first firms to issue guidance on how to use AI when writing applications. Whilst the advice notes that there are a range of potential uses, namely in aiding time management, proofreading, and suggesting amendments to draft answers, the firm were clear that AI should not replace original and unique perspectives.

At the bar, meanwhile, the most recent pupillage recruitment cycle saw candidates applying through the Pupillage Gateway required to confirm that they had not used AI to write their answers.

Applications must be the “sole creation and original work” of the budding barrister, the statement read, with students required to accept that, “any application which has been written with the use of any generative AI LLMs like ChatGPT” will be excluded from the shortlisting process.

Whilst AI can be used in a range of ways to boost productivity and improve existing text, students should be aware of the risks of over reliance on imperfect tech. Last year two US lawyers were fined for relying on cases fabricated by ChatGPT, whilst a litigant in person in the UK received a telling off for citing nine fake authorities to a tax tribunal.

Over the past few months solicitors, barristers, and judges have all received guidance on the use of AI in their respective practices, with over a quarter of lawyers saying that they use AI on a regular basis.

Solicitor Baljinder Singh Atwal examines some of the main concerns

For anyone sceptical of new technology, this is for you.

My previous article explored some of the exciting possibilities of AI within law. From high tech legal research to extremely accurate executive summaries and public databases that could save us a vast amount of time and energy. With the gentle encouragement of the comments section, I now examine some of the main concerns and drawbacks with AI in the workplace.

Trust

With any new technology or method, there will be some reluctance and hesitation around fully implementing it in legal practice. Only once we have some of the most trusted brands and organisations leading the way with AI, do I think it will truly catch on. Consider what happened with virtual events and working during the pandemic — these transformed from tools rarely utilised, to a core practice that has reconceptualised the way we work. Similarly, once the trust is established with AI tools through a process of trying and testing, these are likely to change things forever.

Technology gap

As some organisations and law firms embrace technology quicker than others, we may see a gap in technology which will impact competition, clients, recruitment, retention and more. The improvement of small processes across large organisations will create streamlined work practices: from filling in a form to drafting an email, to legal research and creating a presentation. Through the pandemic, this was also seen where some legal teams adapted very quickly to being able to work remotely, signing documents electronically and conducting meetings with several people in different locations.

Data protection

The use and implementation of AI will often utilise large data sets which may have access to or include very sensitive information. The initial link between data and AI will need to be considered carefully as the growth of technology will inevitably increase cyber attacks, hacking attempts, fraud and more. In an increasingly connected world, international data transfers, privacy and storage will all need to be assessed.

Skills

As we become more reliant on technology and give AI the responsibility of basic admin tasks within the workplace, we may see some skills and practices slowly erode. Some similar examples can be seen through electronic maps and GPS when driving from the traditional skill set of map reading (if anyone reading this is old enough to remember an A to Z). Closer to the office environment was the transition from the written letter to an email. I think that the full implementation of AI at its best may heavily impact: basic legal research, minute/note taking, marketing/branding, preparing first drafts of documents and recruitment methods.

With the new year having been ushered in, it will be interesting to see how AI will change the legal profession and certain sectors. The pandemic brought virtual and remote working into everyone’s working life very quickly. Our knowledge and understanding of how powerful technology can be may give us that encouragement to implement AI quicker despite the above.

Baljinder Singh Atwal is an in-house solicitor at West Midlands Police specialising in commercial and property matters. He is co-chair of the Birmingham Solicitors’ Group and a council member at The Law Society representing junior lawyers nationally.

Pinsent Masons technical lead talks all things AI

“I studied engineering at university and decided I didn’t want to be an engineer – but I liked being a student, so I went back and did a Master’s degree. This was all around the time that the internet showed up on the scene, so I knew I wanted to do something related to technology”, recounts Jason Barnes, low code development technical lead at Pinsent Masons.

Barnes joined the firm before it became Pinsent Masons, originally planning to work for a year or two as he thought up an idea for a PhD. But starting off in a general IT position, he was soon able to get involved in designing databases, often for niche legal work, something he found to be “quite good fun”. Subsequently, web applications came along, and that provided another avenue of interest. “I knew straightaway that this was what interested me, so I effectively became a web application developer. We started building web applications for clients and lawyers, and were met with a good degree of success, so we did more work and our team grew”, says Barnes.

Re-evaluating his career trajectory some years later, Barnes decided to move away from a full-on development role to explore the product management side of things. “In short, this involved looking at how software solutions can be implemented to make things easier for people and businesses”. But missing the creativity of being a developer, Barnes started to get involved with no code/low code tools, such as the Microsoft Power platforms, which came on to the market in a big way a couple of years ago. “I got quite excited with these and was convinced that this was a significant technology direction for us as a firm. Nobody else was spearheading this within Pinsent Masons, so I decided to — now I head up our low code team and am back to being a developer!”

Responding to a question about his day-to-day, Barnes chuckles, saying, “most of the time I have to be stopped — I really do like my job!” He explains that low code tools are designed for non-developers to use and build applications.

“At professional firms, you’ve got, say, a large mass of lawyers who are lawyering and need solutions to help them do this. Now, you can go out to the market to buy these solutions, but for bigger, innovative firms, you want to do this yourself, so you can build exactly what you want. Now, a law firm will only have a certain number of developers, and even they can only do so much when everyone at the firm has an idea they want to see developed. Low code tools can step in and help those people with the ideas to do the development themselves, without having to wait for the developers to do it. So essentially, we’ve got lawyers building robots, although they might not always realise that that’s what they’re doing”.

Barnes sees this as a form of empowerment — with low code tools allowing lawyers to take charge of automating processes and eliminating the frustration of having to wait around for developers to take charge. He does point out, however, that while one can achieve quite a lot with these tools, there’s still some elements that are difficult to navigate, which is where his job steps in, as technical lead of low code development. “We’re there to help the people who have the ideas turn their ideas into solutions”, he summarises.

What’s the typical process through which AI is developed at a law firm like Pinsent Masons? “Despite having worked in technology my whole life, I still always start things off with a pen and paper. If you can’t draw what you want you want to build, then you’re not going to be able to build it”, he responds. Barnes also notes that while lawyers are usually great at articulating what it is they want to build, representing this in a diagrammatic form is often challenging. However, this is at the core of the developer mindset, so we can help with that”, he explains.

Barnes also speaks about the main challenges posed by artificial intelligence (AI) in the legal industry, and he points out that “very few people have a clear understanding of what we mean when we talk about AI”. With the vast majority of people building their views on what they see in the media, most exposure is to generative AI, such as ChatGPT — but that’s only one part of what AI actually is. When I talked earlier about a lawyer having an idea to automate a process, that’s also AI. It’s a computer system doing what a human would normally do. So, one of the challenges is really understanding what it is we’re talking about in the first place”, he explains.

“One of the things at the forefront of everyone’s mind is the protection of client data”, Barnes continues, on the topic of challenges associated with AI in law. “As law firms, nothing matters more than the integrity of our clients’ data — everybody is conscious of the risk of having large language models trained on data sets comprised of client data without prior client agreement”, he notes. On the flipside, Barnes notes that the greatest opportunity for AI in the legal industry is in reimagining the everyday, and taking the monotonous tasks off lawyers hands, so that they are freed up to tap into their human intelligence, to provide better legal services for clients. He offers up document extraction as a tangible example of where AI can have application.

Approaching the end of our conversation, Barnes offers his views on the ‘are lawyers going to be replaced by robots’ debate. “Part of me thinks, well yeah”, he laughs. “A lot of the work I do is around innovation, driving up quality and lowering the cost base. So, taking that to its logical conclusion, we could be looking at a world where we do things artificially across all industries and save a lot of money. But I don’t think anyone wants that”, observes Barnes. While quantifying things in terms of processes and diagrams is easy, and might foretell an automated future, he notes that this ignores the human element of interpersonal relationships which is crucial in the legal space. “I don’t think legal work can be reduced to a collection of ones and zeros”, he concludes.

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Puja Patel, University of Cambridge law graduate, offers an analysis into whether the UK’s current anti-discrimination laws are fit for purpose in the wake of AI

Imagine if popular BBC TV series, The Apprentice, had a robot instead of Lord Sugar sitting in the boardroom, pointing the finger and saying ‘you’re fired.’ Seems ridiculous, doesn’t it?

Whilst robots may not be the ones to point the finger, more and more important workplace decisions are being made by artificial intelligence (‘AI’) in a process called algorithmic decision-making (‘ADM’). Indeed, 68% of large UK companies had adopted at least one form of AI by January 2022 and as of April 2023, 92% of UK employers aim to increase their use of AI in HR within the next 12-18 months.

Put simply, ADM works as follows: the AI system is fed vast amount of data sets (‘training data’) upon which it models its perception of the world by drawing correlations between data sets and outcomes. These correlations then inform decisions made by the algorithm.

At first glance, this seems like the antithesis of prejudice. Surely a ‘neutral’ algorithm which relies only upon data would not discriminate against individuals?

Sadly, it would. Like an avid football fan who notices that England only scores when they are in the bathroom and subsequently selflessly spends every match on the toilet, ADM frequently conflates correlation with causation. Whilst a human being would recognise that criteria such as your favourite colour or your race are discriminatory and irrelevant to the question of recruitment, an algorithm would not. Therefore, whilst algorithms do not directly discriminate in the same way that a prejudiced human would, they frequently perpetrate indirect discrimination.

Unfortunately, this has already occurred in real life — both Amazon and Uber have famously faced backlash for their allegedly indirectly discriminatory algorithms. According to a Reuters report, members of Amazon’s team disclosed that Amazon’s recruitment algorithm (which has since been removed from Amazon’s recruitment processes) taught itself that male candidates were preferable. The algorithm’s training data, according to the Reuters report, comprised of resumes submitted to Amazon over a 10-year period, most of whom were men; accordingly, the algorithm drew a correlation between male CVs and successful candidates and so filtered CVs that contained the word ‘women’ out of the recruitment process. The Reuters report states that Amazon did not respond to these claims, other than to say that the tool ‘was never used by Amazon recruiters to evaluate candidates’, although Amazon did not deny that recruiters looked at the algorithm’s recommendations.

Similarly, Uber’s use of Microsoft’s facial recognition algorithm to ID drivers allegedly failed to recognise approximately 20% of darker-skinned female faces and 5% of darker-skinned male faces, according to IWGB union research, resulting in the alleged deactivation of these drivers’ accounts and the beginning of a lawsuit which will unfold in UK courts over the months to come. Microsoft declined to comment on ongoing legal proceedings whilst Uber says that their algorithm is subject to ‘robust human review’.

Would UK anti-discrimination law protect you?

Section 19 of the Equality Act (‘EA’) 2010  governs indirect discrimination law. In simple terms, s.19 EA means that it is illegal for workplaces to implement universal policies which seem neutral but in reality disadvantage a certain protected group.

For example, if a workplace wanted to ban employees from wearing headgear, this would disadvantage Muslim, Jewish and Sikh employees, even though the ban applied to everyone – this would therefore be indirectly discriminatory, and unless the workplace could prove this was a proportionate means of achieving a legitimate aim, they would be in breach of s.19 EA.

But here’s the catch. The EA only applies to claimants from a ‘protected group’, which is an exhaustive list set out at s.4 EA: age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex and sexual orientation.

The Amazon and Uber claimants fall into the protected categories of ‘sex’ and ‘race’ respectively. Therefore, the EA will protect them – in theory. In reality, it is very difficult to succeed in a claim against AI, as the claimants are required by the EA to causally connect the criteria applied by the algorithm with the subsequent disadvantage (e.g. being fired). It is often impossible for claimants to ascertain the exact criteria applied by the algorithm; even in the unlikely event that the employer assists, the employer themselves is rarely able to access this information. Indeed, the many correlations algorithms draw between vast data sets mean that an algorithm’s inner workings are akin to an ‘artificial neural network’. Therefore, even protected group claimants will struggle to access the EA’s protection in the context of ADM.

Claimants who are discriminated against for the possession of intersectional protected characteristics (e.g. for being an Indian woman) are not protected as claimants must prove that the discrimination occurred due to one protected characteristic alone (e.g. solely due to either being Indian or a woman). ‘Intersectional groups’ are therefore insufficiently protected despite being doubly at risk of discrimination.

And what about the people whom are randomly and opaquely grouped together by the algorithm? If the algorithm draws a correlation between blonde employees and high performance scores, and subsequently recommends that non-blonde employees are not promoted, how are these non-blonde claimants to be protected? ‘Hair colour’ is not a protected characteristic listed in s.4 EA.

And perhaps most worryingly of all — what about those individuals who do not know they have been discriminated against by targeted advertising? If a company uses AI for online advertising of a STEM job, the algorithm is more likely to show the advert to men than women. A key problem arises — women cannot know about an advert they have never seen. Even if they find out, they are highly unlikely to collect enough data to prove group disadvantage, as required by s.19 EA.

So, ultimately – no, the EA is unlikely to protect you.

 Looking to the future

It is therefore evident that specific AI legislation is needed — and fast. Despite this, the UK Government’s AI White Paper confirms that they currently have no intention of enacting AI-specific legislation. This is extremely worrying; the UK Government’s desire to facilitate AI innovation unencumbered by regulation is unspeakably destructive to our fundamental rights. It is to be hoped that, following in the footsteps of the EU AI Act and pursuant to the recommendations of a Private Member’s Bill, Parliament will be inclined to at least adopt a ‘sliding-scale approach’ whereby high-risk uses of AI (e.g. dismissals) will entail heavier regulation, and low-risk uses of AI (e.g. choosing locations for meetings with clients) will attract lower regulation. This approach would safeguard fundamental rights without sacrificing AI innovation.

Puja Patel is a law graduate from the University of Cambridge and has completed her LPC LLM. She is soon to start a training contract at Penningtons Manches Cooper’s London office. 

Bird & Bird senior associate Will Bryson discusses his work in the firm’s tech transactions team

“I really enjoy negotiating contracts that everyone is happy with,” says Will Bryson, senior associate in Bird & Bird’s tech transactions team. Having initially flirted with the idea of being an IP lawyer, Bryson quickly understood that he preferred the commercial tech space. “For the past few years, we have really been talking about artificial intelligence (AI) for its transformational impact on society. I am very passionate about solving legal challenges that lie at the heart of this change.”

As part of his role in the tech transactions team, Bryson often helps businesses looking to buy technology products. “Our clients do not always understand the complexities of the technological tools that they are acquiring and deploying. This is where tech lawyers step in,” he says.

For example, Bryson’s team recently helped a large consumer goods company in buying an Internet of Things (IoT) platform. Essentially, the client wanted to acquire the full tech stack that would underpin their software across all devices. At the end of this successful deployment, the client’s goal was to improve their functionality via a collective network of devices.

“Our role as lawyers in such transactions can often come in multiple capacities,” Bryson explains. “Often the clients want to adopt AI tools but are worried about its risks. They are unsure of what they can and cannot use the AI for. We help them understand the license and use terms, and work with them to understand the risk profile of the asset. This enables them to make an informed decision about internal use of the tool,” he says. A lot of Bryson’s clients have successfully used generative AI tools for various business functions, including things like marketing and branding.

Once the client decides to incorporate AI within their businesses, they might seek support to procure these tools from suppliers. “For such clients, we are involved in the procurement of relevant technologies. To this end, we would typically negotiate technology contracts between the buyers and sellers, making sure the terms work for our clients,” says Bryson.

Legal challenges with AI

But the negotiation of these technology contracts is far from simple, according to Bryson. There are a plethora of legal issues cutting across different areas of law.

“One of the primary issues is that of fault attribution — i.e., who takes the blame when things go wrong?” he says. “Generative AI like ChatGPT often tend to hallucinate, meaning that they can produce inaccurate or illogical results. The main question that we as lawyers drafting these contracts face is to apportion risks between parties if such events happening. We consider how much pressure can be put on suppliers in terms of warranties and obligations if their AI makes mistakes.”

Another issue around AI fallibility is that of ‘causation’, or tracing the reason behind the technical glitch. Bryson explains this further: “Effectively these models are black boxes. They train on vast amounts of data that will enable AI to make its decisions and predictions, but you cannot tie a particular outcome with a particular input or dataset. Who should accept fault, when no one really knows what caused the problem, is a thorny issue in contractual drafting.”

Using ChatGPT wisely

Problem areas do not end here. Generative AI is often questioned from an intellectual property (IP) perspective too. “There are big questions around ownership of the outputs of generative AI systems,” says Bryson. “Parties are used to using contracts to allocate ownership of intellectual property rights in outputs from a service, but where those outputs are created by an AI there may not be any IP to own! If there is nothing to own, what protections should be built into the contract for you is another question that we address for our clients.”

Amidst concerns around data privacy and confidentiality, Bryson is quite hopeful about the future of such tools. “Lawyers have been putting our data into computers and IT systems for decades, so this is not a novel problem at all. It’s about whether we are conducting this exercise in a safe manner,” he says. “There are concerns as to whether the data you feed into the system is being re-used (for example, for further training the system) and so could be disclosed to third parties. Providers of AI solutions clearly recognise this concern and many versions now allow you to ‘opt out’ from your data being reused. This should hopefully take care of some of these confidentiality-related concerns.”

Commercial awareness and careers advice

Ahead of his appearance this afternoon’s Legal Cheek event, Bryson also shares his top tips for students interested in the tech space. “Boosting your commercial awareness is a great way to demonstrate your interest in this area,” Bryson says. “I would encourage students to follow news publications around technology as the landscape changes very quickly. I have built some news reading time into my daily schedule, where I would read from sources like arsTechnica, Financial Times and the Wired magazine. Newsletters like that of Benedict Evans are also a great place to follow interesting trends.”

Alongside developing commercial awareness, Bryson also advises students to be passionate about the field. “Eventually, your enthusiasm is going to shine through at assessment centres,” he says. “Firms love to see candidates who have researched them and know where their strategies lie. But that’s not enough. If you want to really stand out from the crowd, you must also make a case about how your passion and ambition align with that of the firm you are applying to.”

Will Bryson will be speaking at ‘ChatB&B: The Power of AI in Law – with Bird & Bird’, a virtual student event taking place THIS AFTERNOON (Tuesday 17 October). This event is now fully booked. Check out our upcoming fairs and student events.

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Nottingham PPE student Charlie Downey looks at the challenges around artificial intelligence

While the world lauds the latest developments in artificial intelligence (AI) and students celebrate never having to write an essay again without the aid of ChatGPT, beneath the surface, real concerns are developing around the use of generative AI. One of the biggest is the potential for bias. This specific concern was outlined by Nayeem Syed, senior legal director of technology at London Stock Exchange Group (LSEG), who succinctly warned, “unless consciously addressed, AI will mirror unconscious bias”.

 In terms of formal legislation, AI regulation differs greatly around the world. While the UK has adopted a ‘pro-innovation approach’, there still remain concerns around bias and misinformation.

Elsewhere, the recently approved  European Union Artificial Intelligence Act (EU AI Act) will be seen as the first regulation on artificial intelligence. This is expected to set the standard for international legislation around the world, similar to what occurred with the EU’s General Data Protection Regulation (GDPR). The AI Act incorporates principles that will help reduce bias, such as training data governance, human oversight and transparency.

In order to really understand the potential for bias in AI, we need to consider the origin of this bias. After all, how can an AI language model exhibit the same bias as humans? The answer is simple. Generative AI language models, such as OpenAI’s prominent ChatGPT chatbot, is only as bias-free as the data it is trained on.

Why should we care?

Broadly speaking, the process for training AI modes is straightforward. AI models learn from diverse text data collected from different sources. The text is split into smaller parts, and the model predicts what comes next based on what came before by learning from its own mistakes. While efforts are made to minimise bias, if the historical data that AI is learning from contains biases, say, systemic inequalities present in the legal system, then AI can inadvertently learn and reproduce these biases in its responses.

In the legal profession, the ramifications of these biases are particularly significant. There are numerous general biases AI may display related to ethnicity, gender and stereotyping, learned from historical texts and data sources. But in a legal context, imagine the potential damage of an AI system that generated its responses in a manner which unfairly favours certain demographics, thereby reinforcing existing inequalities.

One response to this argument is that, largely, no one is advocating for the use of AI to build entire arguments and generate precedent, at least not with generative AI as it exists in its current form. In fact, this has been shown to be comically ineffective.

So how serious a threat does the potential for bias actually pose in more realistic, conservative uses of generative AI in the legal profession? Aside from general research and document review tasks, two of the most commonly proposed, and currently implemented, uses for AI in law firms are client response chatbots and predictive analytics.

In an article for Forbes, Raquel Gomes, Founder & CEO of Stafi – a virtual assistant services company – discusses the many benefits of implementing automated chatbots in the legal industry. These include freeing up lawyers’ time, reducing costs and providing 24/7 instant client service on straightforward concerns or queries.

Likewise, predictive analytics can help a solicitor in building a negotiation or trial strategy. In the case of client service chatbots, the dangers resulting from biases in the training data is broadly limited to inadvertently providing clients with inaccurate or biased information. As far as predictive analysis is concerned, however, the potential ramifications are much wider and more complex.

An example

Let’s consider a fictional case of an intellectual property lawyer representing a small start-up, who wants to use predictive analysis to help in her patent infringement dispute.

Eager for an edge, she turns to the latest AI revelation, feeding it an abundance of past cases. However, unknown to her, the AI had an affinity for favouring tech giants over smaller innovators as its learning had been shaped by biased data that leaned heavily towards established corporations, skewing its perspective and producing distorted predictions.

As a result, the solicitor believed her case to be weaker than it actually was. Consequently, this misconception about her case’s strength led her to adopt a more cautious approach in negotiations and accept a worse settlement. She hesitated to present certain arguments, undermining her ability to leverage her case’s merits effectively. The AI’s biased predictions thus unwittingly hindered her ability to fully advocate for her client.

Obviously, this is a vastly oversimplified portrayal of the potential dangers of AI bias in predictive analysis. However, it can be seen that even a more subtle bias could have severe consequences, especially in the context of criminal trials where the learning data could be skewed by historical demographic bias in the justice system.

The path forward

 It’s clear that AI is here to stay. So how do we mitigate these bias problems and improve its use? The first, and most obvious, answer is to improve the training data. This can help reduce one of the most common pitfalls of AI: overgeneralisation.

If an AI system is exposed to a skewed subset of legal cases during training, it might generalize conclusions that are not universally applicable, as was the case in the patent infringement example above. Two of the most commonly proposed strategies to reduce the impact of bias in AI responses are: increasing human oversight and improving the diversity of training data.

Increasing human oversight would allow lawyers to identify and rectify the bias before it could have an impact. However, easily the most championed benefit of AI is that it saves time. If countering bias effectively necessitates substantial human oversight, it reduces this benefit significantly.

The second most straightforward solution to AI bias is to improve the training data to ensure a comprehensive and unbiased dataset. This would, in the case of our patent dispute example, prevent the AI from giving skewed responses that leaned towards established corporations. However, acquiring a comprehensive and unbiased dataset is easier said than done, primarily due to issues related to incomplete data availability and inconsistencies in data quality.

Overall, while a combination of both these strategies would go a long way in mitigating bias it still remains one of the biggest challenges surrounding generative AI. It’s clear that incoming AI regulation will only increase and expand in an attempt to deal with a range of issues around the use of this rapidly rising technology. As the legal world increases its use of (and reliance on) generative AI, more questions and concerns will undoubtedly continue to appear over its risks and how to navigate them.

Charlie Downey is an aspiring solicitor. He is currently a third-year philosophy, politics and economics student at the University of Nottingham.

Jake Fletcher-Stega, a recent University of Liverpool law grad, explores the potential for technology to enhance legal services

Utilising advancements like artificial intelligence (AI) and chatbots in the UK can greatly boost efficiency and accessibility in the legal system. Legal tech has the potential to substantially elevate the quality of legal services, prioritising client outcomes over traditional methods, which is crucial for advancing the legal field.

Inspired by Richard Susskind’s work (a leading legal tech advisor, author and academic), this article seeks to demonstrate AI’s potential to spearhead advancements in the legal field and provide solutions to the issue of court backlogs currently plaguing the UK system.

 The problem: the overloaded UK court system

Despite our faith in the right to access to justice as a cornerstone of the British legal framework, the reality is that this is far less certain than might appear. Briefly put, access to justice is the ability of individuals to assert and safeguard their legal rights and responsibilities. In 2012, the Legal Aid, Sentencing and Punishment of Offenders Act (LASPO) significantly reduced funding for the UK justice system, resulting in a current backlog of approximately 60,000 cases and leaving many unable to afford representation.

If we are to fix this ongoing crisis, a fresh, unique, and revolutionary solution is required. I suggest that adopting an innovative approach, such as the use of legal technology, could significantly improve access to justice.

 The solution: legal tech

To echo the view of leading academic Susskind, the legal delivery service is outdated and overly resistant to technological advancements. He asserts that the utilisation of artificial intelligence, automation, and big data has the potential to revolutionise the methods through which legal services can be provided and executed. I must reiterate, it isn’t beneficial that our legal sector is overly conservative and technophobic. Other professions have moved forward with technology, but lawyers haven’t.

Lawyers are behind the curve when compared to other sectors such as finance and medicine who are now utilising technology such MicrosoftInnerEye. Law isn’t significantly different from medical and financial advice. Not different enough to deny the value of innovating our legal services.

The belief that the legal field cannot innovate in the same way as other industries due to its epistemological nature is a common misconception. Many argue that AI will never fully replicate human reasoning, analysis, and problem-solving abilities, leading to the assumption that it cannot pose a threat to human professionals whose job primarily involves ‘reasoning’. However, this perspective is flawed.

While AI may not operate identically to humans, its capability to perform similar tasks and achieve comparable outcomes cannot be underestimated. Instead of fixating on the differences in the way tasks are accomplished, we should shift our focus to the end result.

Embracing AI/Legal Tech and its potential to augment legal services can lead to more efficient, accessible, and effective outcomes for clients, without entirely replacing the valuable expertise and experience that human professionals bring to the table. It is by combining the strengths of AI with human expertise that we can truly revolutionise the legal sector and improve access to justice for all.

Outcome thinking

As lawyers, we must begin to approach the concept of reform in law through the notion of ‘outcome thinking’. In outcome thinking, the emphasis is on understanding what clients truly want to achieve and finding the most effective and efficient ways to deliver those outcomes. The key idea is that clients are primarily interested in the results, solutions, or experiences that a service or product can provide, rather than the specific individuals or processes involved in delivering it.

For example, instead of assuming that patients want doctors, outcome thinking suggests that patients want good health. Another example is the creation of this article. I used AI tools to help me adjust the language, structure and grammar of this text to make it a smoother read. This is because ultimately as the reader you are only interested in the result and not how I crafted this text.

Lawyers are getting side-tracked

Lawyers fail to grasp the focus of this discussion. To illustrate this, let me share a personal story. Just moments before my scholarship interview at one of the Inns of Courts, I was presented with two statements and asked to argue for or against one of them within a five-minute timeframe. One of the statements posed was, ‘Is AI a threat to the profession of barristers?’ Instead of taking sides, I chose to argue that this question was fundamentally flawed.

My contention was that the more critical consideration should be whether new technology can enhance efficiency in the legal system, leading to more affordable and accessible access to justice. The primary focus of the law should be to provide effective legal services rather than solely securing an income for barristers, just as the priority in medicine is the well-being of patients, not the financial gains of doctors.

When a new medical procedure is introduced, the main concern revolves around its impact on patients, not how it affects the workload of doctors. Similarly, the legal profession should prioritise the interests of those seeking justice above all else.

One example — chatbots

One practical example of legal tech that Susskind suggests is the implementation of a ‘diagnostic system’. This system uses an interactive process to extract and analyse the specific details of a case and provide solutions. This form of frontline service technology is often provided through the medium of a chatbot. As a chatbot can work independently and doesn’t require an operator, it has the potential to streamline legal processes.

To test this, I developed a prototype application that demonstrated the potential of AI to tackle legal reasoning. Using the IBM Watson Assistant platform and academic theory from Susskind & Margaret Hagan, I created a chatbot that assisted a paralegal in categorising a client’s case. Although far from perfect, the project proved that AI can substantially improve the efficiency and quality of our outdated legal services.

Concluding thoughts

This article has attempted to demonstrate how embracing technological innovation can revolutionise the legal profession. By focusing on delivering efficient and client-centric outcomes, the legal sector can improve access to justice and create a more effective system. While challenges exist, proactive adoption of innovative solutions will shape a promising future for law, ensuring its continued role in upholding justice for all.

Jake Fletcher-Stega is an aspiring barrister. He recently graduated from the University of Liverpool and his research interests lie in legal tech and AI.