Guest post by Fiona Campbell, director of City law firm Fieldfisher, and Jon Fowler and Lee Reason, managing director and director respectively at disputes consultancy Secretariat
Fiona Campbell
The English legal system, steeped in traditional values, stands as one of the most distinguished common law judicial establishments, with roots tracing back to historic times.
Whilst the UK courts have shown resistance to change over time, in the past decade they have embraced the use of some technologies that naturally improve efficiency. These technologies include CE-filing, online hearings, robo-courts, and digital trial bundles, to name but a few.
Now we are in the age of artificial intelligence (AI). What does the future hold for legal AI?
Legal research
The integration of AI tools into lawyers’ daily routines is poised to increase significantly. Giving the Bar Council’s 20th Annual Law Reform Lecture last year, Master of the Rolls Sir Geoffrey Vos underscored the credibility of AI opinions, especially in legal contexts, highlighting their superiority over generic chatbots in accuracy and reliability.
As these tools continue to advance, their adoption across law firms is expected to rise. AI solutions refine language, strengthen legal arguments, and enhance legal research through features integrated into legal search engines and databases.
However, it is essential to recognise the limitations of certain tools, such as Chat GPT, in legal practice. Firstly, sensitive data subject to litigation must never be shared or uploaded to open forums for legal augmentation. Additionally, Chat GPT lacks specialisation for litigation purposes, unlike dedicated market tools.
Legal submissions
Lord Denning once said: “Words are the lawyers’ tools of trade.”. At present, the manual input required of lawyers when drafting pleadings, witness statements, and other submissions and court documentation is demanding.
The courts have endorsed the use of AI to assist lawyers with such demands, with the caveat that practitioners are responsible for material produced in their names, and therefore AI tools should be used appropriately, taking steps to mitigate any risks.
Case outcome predictions
Jon Fowler
Machine learning techniques analyse judicial decisions and predict case outcomes with precision, utilising tailored algorithms to enhance predictive accuracy.
This capability extends the practical utility of case outcome prediction significantly to litigants, allowing them to evaluate the viability of their claims before commencing legal proceedings. Moreover, it proves invaluable in settlement negotiations, providing insightful data to guide strategic decisions.
Robo-courts/AI in the digital justice system
Online courtrooms have become increasingly accessible for both criminal and civil matters. Criminal cases can now be addressed online, allowing individuals to pay court fines or make pleas for traffic offences.
Similarly, civil parties can file monetary claims electronically, and robo-courts have been established for small claims, certain tax remedies, and specific family law matters like divorce applications.
During the McNair Lecture at Lincolns Inn last April, Sir Geoffrey Vos pondered the potential future where AI may assume responsibility for decisions, even in minor cases.
He emphasised the importance of implementing controls in such scenarios, ensuring transparency for parties involved and maintaining the option for appeal to a human judge.
While acknowledging the necessity for confidence in any judicial system, Sir Geoffrey highlighted the need for caution, particularly in cases involving sensitive matters like child welfare. However, he stressed the potential for AI to enhance the efficiency of commercial decisions provided it is subject to appropriate oversight.
Sir Geoffrey has outlined plans for the UK courts to integrate AI into the digital justice system, proposing the establishment of a digital platform offering an AI-driven pre-action portal or dispute resolution forum.
This initiative aims to provide citizens and businesses with ‘ELSA’ or Early Legal Services and Advice, leveraging AI to deliver accurate and timely assistance.
The successful implementation of this initiative will require collaboration between the Ministry of Justice, HM Courts and Tribunals Service (HMCTS), the judiciary, the Office for Professional Body Anti-Money Laundering Supervision, and the legal profession.
The evolution of digital forensics and e-disclosure
Lee Reason
Digital forensics is a branch of forensic science that involves the collection, analysis, and preservation of electronic evidence that has often acted as the precursor to data being prepared for disclosure exercises. It encompasses the examination of digital devices, networks, and data to uncover traces of malicious activities, identify perpetrators, and support legal proceedings.
AI innovations are revolutionising this field in a number of ways. Traditional methods of sifting through vast amounts of digital evidence were time-consuming and labour-intensive. AI-powered tools, however, can swiftly process and categorise massive datasets, identifying patterns and anomalies that might elude human investigators.
Machine learning algorithms play a crucial role in enhancing the efficiency and accuracy of both digital forensics and e-disclosure. These algorithms can learn from historical data to recognise commonalities and signatures within datasets, helping investigators identify potential areas of interest more rapidly.
Additionally, AI can assist in the automated detection of suspicious activities, allowing for proactive threat mitigation and reducing response times.
Natural language processing (NLP) is another AI-enriched capability, primarily benefiting e-disclosure. NLP enables the analysis of unstructured data, such as text messages and social media communications, helping experts extract valuable insights from these sources.
Sentiment analysis, entity recognition, and language pattern analysis are all aspects of NLP that contribute to a more comprehensive understanding of digital evidence.
AI is instrumental in the development of advanced forensic tools for image and video analysis. Deep learning algorithms can automatically detect and classify objects, faces or even illicit content within multimedia files, expediting the identification of relevant evidence.
This not only saves time but also ensures a more thorough examination of multimedia data, contributing to the overall efficacy of investigations.
Remote hearings
HMCTS aims to implement a new video hearings service in 2024, marking a pivotal step forward in modernising court proceedings.
Conclusion
The significance of AI in litigation is undeniable, and its influence is poised to continue shaping the legal landscape.
This integration will undoubtedly impact the types of disputes requiring resolution, the methods of resolution with AI assistance, and the selection of the most suitable tools for each scenario.
These profound and swift changes will usher in unprecedented methods of practice, fundamentally reshaping the future of litigation.
However, it’s crucial to emphasise that AI’s presence does not signify the decline of qualified lawyers or their role in the legal domain. On the contrary, the immense volumes of data involved in today’s legal proceedings necessitate legal professionals equipped with the skills and knowledge to leverage and understand AI’s capabilities effectively.
This collaboration between legal practitioners and AI is essential for achieving optimal outcomes in an ever-evolving legal landscape. As the legal profession embraces this transformation, AI has the potential to become a powerful tool, augmenting the expertise of lawyers and fostering a more efficient, accessible, and just legal system for all.
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