Beth Gilmour explores the potential benefits and limitations of using AI to increase access to justice in the winning article of the SCL AI Group Junior Lawyer Article Competition
Introducing Disruption
Imagine someone, sitting anxiously in a waiting room at a solicitor’s office they hastily found online. They clutch a notice of eviction in their hands, confused as to how their landlord can remove them from their home of ten years. Somewhere else, a woman waits on hold with a personal injury helpline after months of excruciating pain from slipping on an uneven paving stone, leaving her unable to work. Another man is on their commute home from work for the last time, frantically googling employment law, after being unexpectedly dismissed; he does not know what he can do or where next month’s rent will come from. Despite their differences, they all share one common question:
“Am I going to win?”
It is an age-old query coming in different forms; can I stay in my home; how much will I get in damages? Ultimately, they want to know about the outcome.
When considering how AI can increase access to justice, we must start there—with the outcome. Too often, solutions to the crisis of access to justice are framed within the constraints of existing systems, which rely on the current process being supported by technology (sustaining technologies). While these efforts are valuable, artificial intelligence (“AI”) offers an opportunity to work outside of these constraints and construct new routes to the same just outcomes (disruptive technologies).1 This account will consider how access to justice has already benefitted from AI and what could be done next.
Organising Disruption: Efficiency
For AI-driven disruption to meaningfully increase access to justice, its implementation must be guided by clear principles with efficiency as the cornerstone. In this context, efficiency encompasses both expediency and accuracy in results. Timely justice has been a consistent guiding ethos, with the Magna Carta stating, “To no one will we sell, to no one deny or delay right or justice.”2 This remains pertinent today, as explained by Zuckerman, the passage of time can diminish the value and enforceability of rights, making speed an essential element not just in procedure but in the dispensing of justice.3
Guided by efficiency and a willingness to move beyond traditional processes, AI can increase access to justice by providing faster, and still accurate, resolution that would otherwise take months of litigation.
Implementing Disruption
One clear example of AI systems enhancing access to justice is the rise of chatbots, such as AccessAva, which streamline legal information for those who need it most.4 AccessAva, developed by Carers UK in partnership with Access Social Care, is an online tool designed specifically for unpaid carers in the UK. It empowers users by providing easy-to-understand legal information, along with templates and resources which reduce the need for professional legal assistance. Other models include DoNotPay, which offers a similar service aimed at consumers. These chatbots are examples of Large Language Models (“LLM”), which are trained on large amounts of text data, which, in turn, generate natural language responses to a wide range of inputs.5 What sets models like DoNotPay and AccessAva apart is their focus on supporting litigants directly. They disrupt by allowing computers to speak the language of lawyers, which was previously unachievable. Further, they increase efficiency by centralising the information needed by the individual and making justice more accessible to those who might otherwise struggle with traditional systems due to financial constraints or a lack of understanding. This exemplifies AI as a solution to problems highlighted by the 2023 Legal Needs Survey by the Law Society. A key finding was that, of people who faced a legal issue between 2019 and 2023, only 52% received professional help, with the rest either relying on family and friends or not seeking assistance at all.6 The survey highlights two key barriers: the cost of legal advice and a lack of understanding or confidence in engaging with the law. AI-driven platforms like AccessAva are precisely the kind of innovation that can overcome these obstacles, closing the gap and providing essential support to those who would otherwise struggle to access justice.
They can also be taken further. AI systems, particularly those using machine learning, can analyse patterns in large datasets to predict outcomes which has the potential to take chatbots beyond the provision of legal information and into the realm of advice.7 For instance, researchers have used AI to predict the outcomes of European Court of Human Rights cases with 79% accuracy.8 A predictive capability like this has the potential to disrupt as it would allow an individual not only to understand what the next steps are but to make a well-informed decision on whether to pursue at all. Users could ask whether they have a strong case, whether pursuing it is cost-effective, or what outcomes they might expect. Accessing legal advice is a key element of access to justice and an AI system which combines predictive outputs with user-friendly interfaces, like AccessAva and DoNotPay, has the potential to increase the number of people that such advice is available to.
Challenges
There are concerns which should be met head-on, the primary one being accuracy. If the architecture which underlies any predictive technology is wrong, the output will be too. Thus, any such model would have to be tightly regulated by humans (lawyers) with the knowledge of the underlying area and the ability to understand the dispute to ensure the algorithm does not result in litigants abandoning worthy claims. Legal minds will have a role at the point of data entry and in auditing the output. The changing role of the lawyer and the need for the legal sector to be reflexive with technological advancements is part of the disruption that access to justice solutions which use AI will bring around. The lawyer’s auditing role is also in identifying “hallucinations” by chatbots whereby responses generated are incorrect or fabricated. The risk is lower with bespoke systems using specialised legal data than with general-purpose chatbots like ChatGPT.9 Despite the reduced risk, verification by a qualified legal mind is still necessary to ensure accuracy, and therefore efficiency. With this safeguard, chatbots can help democratise legal assistance.
Another significant concern regarding the use of AI in legal practice is that it could stunt the growth of the common law. A classic iteration of this concern comes from considering Donoghue v Stevenson,10 a seemingly simple case where a woman drank from a bottle which, unbeknownst to her, had a decomposing snail inside. While the case involved a straightforward fact pattern, it went all the way to the House of Lords and ultimately established the “neighbour principle,” a key development in negligence law. If fed into an AI advice system before this principle was established, the outcome might have been different, possibly failing to recognise the broader legal implications of the case. This raises the concern that AI systems, by relying heavily on data from past decisions, might overlook the unique factors in a case that could lead to the establishment of new legal principles. If AI simply provides a binary answer—”good claim, pursue” or “no claim, tough luck” – it could ignore the nuanced, creative reasoning that legal professionals bring to the table. It must be acknowledged that whichever AI system is implemented, it has to be sophisticated enough to recognise and flag unique features of a case that may not align with past precedents. These features could prompt lawyers to consider how the case might develop or whether it warrants a new interpretation of the law.
Conclusion
Embracing AI has the potential to reduce delays and empower individuals with legal information and guidance. This disruption must be managed carefully with human oversight to address challenges in ensuring accuracy and preserving the flexibility of legal interpretation.
This account comes from a legal perspective, without the technical expertise to explore the underlying technology. That knowledge gap should not, however, remove lawyers from the conversation. They bring essential industry insights and knowledge that are key to the reflexive relationship between law and tech.
Ultimately, with such collaboration and safeguards, AI can bridge the justice gap, ensuring that more people can ask and answer the crucial question, “Am I going to win?”
Beth Gilmour is the winner of the SCL AI Group Junior Lawyer Article Competition. Beth is a BAR student and is currently a Judicial Assistant to High Court Judges in England and Wales.
- Susskind RE, Tomorrow’s Lawyers an Introduction to Your Future, Chapter 6 “Disruptive Legal Technologies” (Third edition, Oxford University Press 2023), ↩︎
- Magna Carta Clause 40 ↩︎
- Professor Adrian Zuckerman, Zuckerman on Civil Procedure: Principles of Practice 4th Ed, Chapter 1 p.17 (4th Edition. Street & Maxwell, 2021) ↩︎
- AccessAva, available at https://www.accesscharity.org.uk/accessava (accessed December 2024) ↩︎
- Robin Allen KC and Dee Master, Judges, Lawyers, and litigation: Do they, should they, use AI? Paper for the Employment Law Bar Association (2024) p.17 ↩︎
- The Law Society: Find out what your clients need, with the results of our Legal Needs Survey, available at https://www.lawsociety.org.uk/topics/research/find-out-what-your-clients-need-with-the-results-of-our-legal-needs-survey (accessed December 2024) ↩︎
- Richard Susskind n1, Table 6.1 ↩︎
- Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, Vasileios Lampos, Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective, (2016) PeerJ Computer Science 2:e93 ↩︎
- Robin Allen KC and Dee Master n5, p.13 ↩︎
- [1932] AC 562 ↩︎
