Are you attending Future Lawyer Week? We’re delighted to sponsor Litigation Day, on 18th April 2024.
Stephen Dowling, Senior Counsel and CEO of TrialView, will be joined by Anna Gilbert, Counsel at Hausfeld, to explore the transformative force of digitisation in case management within litigation. Together, they’ll dissect its end-to-end workflow and unveil the plethora of opportunities it presents for legal professionals.
We hope to see you there!
Over the past year, generative artificial intelligence (Generative AI) in the form of large language models (LLMs) like ChatGPT-4 has taken the world by storm. Legal practice is no exception. Many will recall the headline-making story of a New York lawyer who was sanctioned by a judge for relying upon non-existent case law precedent that he obtained from ChatGPT-4 and did not double-check, as well as the Texas federal court judge who has implemented a standing order that requires all litigants appearing in his courtroom to make a certification concerning the use of Generative AI in their submissions.
Yet, international arbitration practitioners have relied upon tools powered by other forms of artificial intelligence (AI) for many years. Indeed, in an era marked by Big Data and an increasingly complex dispute resolution ecosystem encompassing broad document disclosure and evidentiary collection, the work of arbitration practitioners would be impossible to manage without AI.
So, at a moment dominated by sexy headlines about the risks and opportunities presented by Generative AI in legal processes, this blog post takes a step back to examine the “old hat” AI-powered tools and applications that have long supported international arbitration practitioners with document management, document review, document production, and arbitrator due diligence, among other tasks, and concludes by introducing how Generative AI-powered tools may supplement and enhance the lawyer’s toolkit.
Document review and production are often necessary evils in international arbitration practice. These processes are perceived as time-intensive and low-value grunt work. Moreover, they are often a sore point in the outside counsel and client’s relationship because the client may be unwilling to pay the usual rates for time spent on such work. This pressure point creates an opportunity to implement AI-driven document review tools to reduce the time and costs associated with labour-intensive document review and analysis. E-discovery platforms such as Relativity, Luminance, EverLaw, and CS Disco employ machine learning algorithms to categorise, extract, and analyse information from vast quantities of documents.
Each platform has AI-driven functionalities that enable users to swiftly identify pertinent documents through conceptual search (in addition to keyword search), data visualisation, and document clustering, which expedites the overall document review process.
Conceptual search in the context of e-discovery is a powerful and innovative approach to information retrieval that goes beyond traditional keyword-based searches. Unlike keyword searches, which rely on exact word matches, conceptual search utilises artificial intelligence and natural language processing to understand the underlying concepts and context within documents. Conceptual search is, therefore, useful in e-discovery as it enables legal professionals to uncover relevant documents even when specific keywords or phrases may not have been used, thus reducing the risk of missing critical information.
One of the biggest challenges of document review and production is ensuring that the choices made are consistently reflected across all similar and duplicate iterations of that document. Here, AI-driven functionalities can drive efficiencies and consistency, thereby assuaging lawyers’ concerns over the risks of inconsistent instructions or understandings, or plain old human error.
Data visualisation of related documents presents document relationships, patterns, and key information graphically and offers a visual narrative of the document corpus. This allows legal teams to quickly grasp the structure of the data, identify important trends, and pinpoint critical documents. Moreover, it aids in developing effective case strategies by revealing patterns and connections that may not be immediately apparent through traditional text-based analysis.
These e-discovery platforms also cluster related documents by content or themes, enabling the review of groups of documents relevant to an issue or fact in the arbitration. Clustering also helps identify patterns, trends, or commonalities within a document corpus, which can be crucial for building a coherent legal strategy or uncovering hidden insights. Another benefit of clustering is ensuring that strategic decisions are uniformly and consistently applied to the overall document set, including decisions on the treatment of privileged, confidential, or non-responsive information.
These tools enhance the speed, accuracy, and comprehensiveness of document review for production and disclosure, making them efficient at managing large volumes of electronic data in complex international arbitrations. As experienced counsel will know, these tools enable international arbitrations to unfold on faster and more efficient timetables than would be possible if their legal teams were stuck reviewing, categorising, and producing hard copy documents from within storage boxes or paper filing cabinets.
A key feature of end-to-end online dispute resolution (ODR) platforms like New Era ADR is the ability to automate and streamline dispute resolution. AI algorithms facilitate the intake of cases, intelligently categorise them, and allocate resources efficiently. Parties can initiate arbitration proceedings seamlessly, guided by AI-driven prompts and tools, simplifying the complex legal processes associated with dispute resolution. This level of automation reduces administrative burdens and ensures that cases progress smoothly, saving both time and resources. These features may also democratise the dispute resolution process, making it more accessible for self-represented parties who may not have in-house legal expertise.
Parties can track the progress of their cases in real-time, access relevant documents, and receive notifications through user-friendly interfaces. AI technologies underpin these features, ensuring that parties are well-informed about the status of their arbitration proceedings. This transparency fosters trust and confidence in the process, ultimately contributing to more equitable and satisfactory outcomes.
Platforms like TrialView offer an AI-powered litigation workspace, with smart tools for legal teams to navigate a dispute’s full lifecycle. TrialView provides a centralised platform for uploading, managing and interrogating case data, smart bundling tools that permit a user to create a bundle in seconds (with automatic pagination, tabbing, and indexing). Hyperlinking and cross-referencing tools also run in tandem with in-built court compliance checks, and late insertion features offer greater flexibility. Each of these features can save a lawyer dozens of hours and prevent some of the last-minute stress (and overtime paralegal costs) that crop up on the eve of a hearing or trial.
Meanwhile, TrialView permits the same data to be examined and interrogated using AI intelligent search. A user can ask a question, and AI-powered tools will not only find the answer, but will direct the user to the exact excerpt, paragraph, and document. Entity search tools allow a user to find connections between key dates, actors, and events, with timeline building offering further insights. This capacity to really get to know the evidence is incredibly potent, removing the need to manually trawl through paper files for specific facts and data. Aside from the time and cost savings, these tools allow legal teams to spend more time doing ‘real’ legal work – including focus on strategy, corroboration, and persuasion, as well as allowing time to consider counter arguments and counter approaches.
Another example of AI in action is TrialView’s witness statement preparation and deposition creation tool, which allows users to record the interview, generate an automatic transcript, and convert it to a hyperlinked witness statement.
Finally, on the presentation side of things, TrialView offers smart evidence presentation tools that allow all parties to follow in real-time, with annotation and highlighting tools available to mark up documents and the hearing/trial transcript as the evidence unfolds. Parties may also follow remotely using an integrated video platform and team members in different locations can use built-in AI tools to discern potential material that can corroborate or undermine the propositions being advanced in the hearing/trial room itself with great speed.
AI-powered legal research platforms have become indispensable tools in international arbitration. They provide arbitration lawyers with the technology to search for relevant precedents, jurisprudence, and legal sources across multiple jurisdictions.
Platforms like Kluwer Arbitration and Jus Mundi harness machine learning to compile and enrich their extensive databases of international arbitration cases, treaties, conventions, and related legal documents. AI power offers great benefits to researchers as it underlies the technology that prepares case citations and helps create cross-links and references between various content sets.
CaseText also employs an AI-driven approach, leveraging natural language processing to extract valuable insights from legal texts. Its platform also offers the capability to analyse and summarise legal research results and draft legal memoranda.
LexisNexis and Westlaw, established names in the legal research landscape, also integrate AI into their platforms to enhance research capabilities by providing predictive analytics to suggest relevant cases, statutes, and secondary sources based on user queries.
Interestingly, with the rise of Generative AI, many of these legal research platforms have unveiled new Generative AI-powered LLM chatbots to enable the legal researcher to engage in a question-and-answer exchange to facilitate the legal research journey. Such tools supercharge the legal researcher’s user journey and experience. They are especially valuable to both newer legal researchers (such as students) or those who aim to understand a new area of law very quickly. However, one must not forget that the data and data enrichment that legal researchers encounter have benefitted from AI-driven tools for many years already.
International arbitration is characterised by its inherently cross-border nature. Parties frequently come from different cultural and linguistic backgrounds, and evidence and testimony may be in multiple languages. Despite this added complexity, the usual goal of effective communication and understanding of legal content is paramount to achieving equitable dispute resolution. In this context, AI-powered machine translation has emerged as a game-changing technology, offering advanced linguistic capabilities that transcend traditional language barriers.
DeepL, renowned for its neural machine translation technology, and Google Translate, a widely accessible and versatile translation service, represent two exemplary platforms that leverage AI and deep learning techniques to deliver precise and context-aware translations of legal documents and communications translations.
DeepL, Google Translate, and similar AI-driven translation platforms are indispensable assets in international arbitration. They ensure that all participants can effectively engage in the process regardless of language differences, promoting fairness and impartiality in dispute resolution.
Again, such AI-powered tools are not new to international arbitration practice but have become commonplace in light of the frequency with which parties and their counsel must operate across languages and linguistic barriers.
International arbitration often involves complex disputes with multinational parties and necessitates a rigorous approach to arbitrator selection and conflict management. Various services have emerged to offer arbitrator profile and conflict-checking tools, including Arbitrator Intelligence, Kluwer Arbitration’s Profile Navigator & Relationship Indicator, and Global Arbitration Review’s Arbitrator Research Tool (ART).
Each tool uses a combination of AI, data analytics, and self-reported information to provide comprehensive insights into arbitrators’ performance and track records of arbitrators. This data helps arbitration practitioners identify suitable candidates for arbitrators based on empirical data rather than subjective assessments. Further, engaging in this process may add diversity to the prospective arbitrator pool by bringing additional candidates to the practitioner’s attention who meet the case criteria but who may otherwise not be known by the selecting counsel. Overall, these tools provide data-driven inputs to the arbitrator selection process and promote transparency and objectivity in the arbitrator selection process.
On the other hand, these tools may also introduce subjective insights. In some instances, the data collected may include candid feedback from counsel who have appeared before those arbitrators in particular cases. While these insights would be of a different nature from objective data-driven insights, they may prove equally useful in helping practitioners identify arbitrators who meet their needs and who may otherwise have been unknown to them.
Predictive data analytics has become a powerful tool in litigation finance and third-party funding, transforming legal professionals’ strategic decisions. Notable platforms in this space include Lex Machina and Arbilex.
By aggregating all data from documents filed on court dockets and leveraging AI along with human legal expert review to structure the data, Lex Machina provides insights like the quantum of damages, potential case resolutions, opposing counsel’s litigation history, and timing of the proceedings to enable predictions on various aspects of their cases.
Similarly, Arbilex employs machine learning algorithms to analyse historical case data, legal precedents, and financial metrics. This enables third-party funders to assess the potential risks and rewards of funding a particular case. Burford Capital, a third-party funder, enhances its legal finance modelling with AI. However, it acknowledges the limitations of AI due to the confidentiality of about 90% of commercial disputes, which are resolved through settlement. Despite this, Burford considers that integrating AI can improve assessment accuracy and speed by analysing factors like profitability and outcome likelihoods. However, the effectiveness of AI models depends on the quality of available data, highlighting the difficulty of relying solely on AI tools in legal finance.
These and similar solutions enhance decision-making within international arbitration, serving as a valuable resource for legal teams dealing with cross-border disputes. These tools analyse historical case data to provide insights into settlement probabilities and potential third-party funding opportunities and enable arbitration practitioners to make informed decisions and negotiate settlements more effectively.
Burford Capital also uses AI to originate new business and identify potential cases by enhancing the process of case identification for investment. They leverage AI to combine public data with insights from past successful investments, employing heuristics and prompting techniques. This approach helps Burford to identify lawyers and cases that meet their investment criteria and discover instances where businesses have suffered harm but are unaware of their strong claims. Specifically, Burford has initiated projects to scrape the web for lawyers with specific profiles related to successful case types, thereby streamlining the process of finding new investment opportunities and assisting businesses in recognizing valuable claims.
Notwithstanding the number of AI-driven tools and providers that dispute resolution practitioners are already familiar with and frequently use, there is still ample opportunity, within these same arenas, for innovation driven by Generative AI-powered technology. Eliza, a coauthor of this post, is also the Co-Founder of Lawdify, a solution that creates intelligent systems (AI agents) to run specialised, laborious, and high-stake tasks for legal professionals. She believes that the new generation of AI legal technology platforms like Lawdify will leverage the capability of the LLMs as the “superbrain” to process semantics and to contextualise and connect relevant concepts within legal documents so that a layer of true intelligence is added to the voluminous documentary records that disputes lawyers need to manage, navigate, and learn. These techniques will enable disputes lawyers to quickly generate work products like chronologies, dramatis personae, lists of issues, lists of relevant facts, and cross reference each to the underlying evidentiary documents in seconds. Lawyers will be able to promptly retrieve a document based on conceptual and semantic search (without the need to anticipate specific keywords) with Natural Language Processing. They could also “pivot” the underlying evidentiary record (just like a pivot table in Excel) according to their needs based on a variety of parameters, for example, display the list of documents supportive of a particular fact that bolters the arguments of the claimant on a specific legal issue and sorted chronologically. Indeed, LLMs are proficient at doing this.
The next frontier of AI-powered legal technology solutions could create AI agents that understand the objective of a task and are capable of autonomously running them end-to-end, such as tagging for privilege for responsive documents in a production exercise, creating a privilege log, retrieving relevant and material documents in response to production requests.
With respect to reliability and accuracy, AI-first legal technology companies like Lawdify will adopt techniques borrowed from data science to provide high scores in answer faithfulness (answers based on a real and not made-up fact in the corpus), and answer relevance. They will set up guardrails like providing sources of underlying documents, creating a record of the reasoning behind each action taken by an AI agent and of the considerations leading to such action (like generating a list of documents that were not responsive to a specific request), implementing evaluation stacks to benchmark the answers against ones provided by human lawyers, and a rigorous user feedback loop to collect and monitor user comments and actions.
AI-powered tools are far from new in international arbitration practice. Indeed, for many years, the robust practice and procedural approaches that have become commonplace have been driven by the efficiencies and opportunities that AI-powered tools have enabled.
Yet, Generative AI remains poised to profoundly transform how international arbitration work is performed and offered. Its potential is vast, offering enhanced capabilities in legal research, document review automation, and even altering traditional billing models. The promise of AI-driven end-to-end online dispute resolution platforms could revolutionise how arbitration is approached, particularly for low-value commercial disputes. It has the potential to rebalance how arbitration is practised, opening doors to democratising access to the arbitral process for users who may be self-represented and without in-house legal expertise.
In all events, just like the “old hat” AI-powered tools that have facilitated international arbitration as it exists today, lawyers must be among the early adopters of Generative AI-powered tools, finding ways to test and hone new skill sets to enhance their practices and offer greater value to their clients.
*The opinions and insights presented in this post solely represent the authors’ views. They are not endorsed by or reflective of the policies or positions of their affiliated firms or organisations.
Date: 26th April, 10am. Online Webinar
Speakers: Chaired by our Commercial Director, Eimear McCann, our panel includes Airlie Goodman, Partner at Mayer brown; Hormis Kallarackel, Associate at Mayer Brown; Leon Major, at KL Discovery; and Stephen Dowling, TrialView founder and Senior Counsel.
With the exponential increase in digital data, the need to use technology at a much earlier earlier stage of proceedings is ever more evident. Technology now plays a crucial role in efficiently identifying, categorising, and safeguarding sensitive information in competition law – for example, within the framework of confidentiality rings.
Looking at this through the lens of PD 1/2024*, our panel will explore how technology – which is both the problem and the solution – could be used effectively to manage the increasing complexity of data. Our experts will also look at how collaboration, between all parties, including legal tech suppliers, could offer a real opportunity to navigate these challenges proactively.
*Practice Direction 1/2024 addresses the establishment of confidentiality rings, a mechanism allowing restricted distribution of documents containing sensitive information.
Register here.
We are delighted to run a series of webinars with CIArb and Jus Mundi. The first webinar in our AI and ADR: Theory and Practice series will focus on the current legal technology market, available AI tools and their nature. The panel will also take a critical look at benefits and risks of relying on AI in ADR and explore relevant practical cases.
Speakers: Kateryna Honcharenko MCIArb; Stephen Dowling; Monica Crespo & Johnny Shearman.
AI and ADR: Theory and Practice
New technology has become an integral part of the legal profession over recent years. However, when it comes to artificial intelligence (AI), it has also become a stumbling block. AI tools are capable of significantly increasing our professional efficiency, but unconditional reliance on AI may also adversely affect the procedural efficiency of all forms of private dispute resolution. This includes due process, confidentiality, enforceability and other principal procedural matters. The question many dispute resolution practitioners are asking is: To what extent can AI be used in our work?
Together, we’ll explore the realm of AI, including AI tools and their application. We will discuss how AI can be utilised in an efficient and safe manner, and what parties, counsel and tribunals should consider before using AI tools.
Each webinar focuses on a particular theme, giving you the opportunity to learn from experts in the field. You can then discuss the theme further with expert moderators and peers at a subsequent Let’s Discuss networking event.
This webinar is FREE, register here.
Please note, times listed are in BST (GMT+1).
Don’t forget to register for Let’s Discuss AI Fundamentals in ADR!
In our latest guest post, Elizabeth Chan (Tanner De Witt), Kiran Nasir Gore (Kiran N Gore PLLC), and Eliza Jiang (Lawdify AI) explore the latest developments in the evolving world of Generative AI, with a focus on the need for pragmatic regulation.
Over the past year, the power of generative artificial intelligence (Generative AI) has taken the world by storm. Today, nearly every digital tool and platform is advertising a new Generative AI feature to help users to better organise their tasks, streamline and process information, conduct research and learn more about various topics, and write more efficiently and effectively. Legal processes are not immune from these developments, and a global debate has emerged on whether and what role Generative AI-powered tools should play in the legal work performed by dispute resolution specialists. As this blog post demonstrates, the devil lies in the details. While Generative AI-powered tools can make litigation and arbitration teams more efficient and effective, regulations, such as disclosure or certification requirements, can help (or hinder!) ethical, fair, and responsible use of these tools and a level-playing field for all parties participating in these proceedings. This post explores these latest developments.
Using Generative AI-powered tools in the work of dispute resolution specialists presents many challenges and risks. These tools can be opaque, and it may be challenging for users to understand precisely what they do, how they work, and what happens to the information and data users input. These circumstances create the potential for severe consequences for misinformed or underinformed users, including professional conduct violations or breaches of confidentiality and/or attorney-client privilege. Even more, where disputes, such as international arbitration cases, involve cross-border elements, the laws and regulations of multiple jurisdictions may apply. Indeed, in the multi-jurisdictional context, it may be even more urgent to either harmonise or regulate standards of use for Generative AI-powered tools to help ensure procedural fairness.
The BCLP 2023 survey of 221 arbitration professionals revealed that a significant majority (63%) support regulating disputing parties’ use of Generative AI-powered tools in international arbitration proceedings. This consensus suggests that there are risks associated with non-regulation. This is underscored when one considers the importance of the documents that international arbitration practitioners may work on, including legal submissions, expert reports, and arbitral awards – each of which must be precise, accurate, and coherent. However, while baseline regulation itself is an important first step to engaging with this technology, it is equally vital that the developed regulatory framework is adaptable and forward-looking.
The Silicon Valley Arbitration and Mediation Center’s (SVAMC) Draft Guidelines on the Use of AI in Arbitration (Draft Guidelines) stand out as the only cross-institutional guidelines (to date) tailored explicitly for international arbitration contexts. The SVAMC Draft Guidelines were prepared with contributions from a committee (including Elizabeth, a co-author of this blog post) and propose a nuanced approach to the disclosure of when AI has assisted in preparing legal work product. It is important to note that the SVMAC Draft Guidelines define “AI” broadly. While their immediate focus is on the Generative AI-powered tools that are also the focus of this blog post, the Draft Guidelines refer to “AI” generally and aim to go even further in hopes of remaining evergreen and thereby capturing the regulation of AI-based technologies and tools that may not yet be developed.
The SVAMC Draft Guidelines recognise that the need for disclosure may vary, suggesting that, in some instances, the AI technology being used may be straightforward and uncontroversial (e.g., technology-aided document review (TAR)), thus not requiring explicit disclosure. However, the Draft Guidelines also allow for the possibility that arbitral tribunals, parties, or administering institutions might demand disclosure of the use of Generative AI-powered tools, especially when such use could significantly influence the integrity of the arbitration proceedings or the evidence presented within it.
The AAA-ICDR Principles for AI in ADR (AAA-ICDR Principles) and the MIT Task Force on the Responsible Use of AI in Law (MIT Principles) provide additional sets of guidelines and principles on the use of AI in legal practice. The AAA-ICDR Principles emphasise that AI should be used in alternative dispute resolution (ADR) cases, including arbitrations, in a manner that upholds the profession’s integrity, competence, and confidentiality. They do not specifically address disclosure requirements. Meanwhile, the MIT Principles, which are applicable more broadly within legal contexts, highlight the importance of ethical standards, including confidentiality, fiduciary care, and the necessity for client notice and consent, indirectly suggesting a framework where disclosure of AI use might be required under certain conditions to maintain transparency and trust. These various guidelines and principles collectively underscore the evolving landscape of AI in legal practice and emphasise the need for careful consideration of when and how AI-powered assistance should be disclosed. These guidelines and principles also share the core tenet that the integrity of legal work and fairness in the dispute resolution process must be upheld.
Different jurisdictions are approaching the need to disclose the use of AI assistance in preparing legal work products differently, and a spectrum of regulatory philosophies and practical considerations is emerging. For example, in the United States, a Texas federal judge has added a judge-specific requirement for attorneys to not only certify that their court filings if drafted with the assistance of a Generative AI-powered tool, were also verified for accuracy by a human but also take full responsibility for any sanction or discipline that may result from improper submissions to the court. This approach demonstrates a policy-based choice. The objective is not to prevent the use of Generative AI-powered tools in litigation practice but rather to allocate risk, maintain the integrity of the materials put before the court, and ensure that attorneys remain ultimately responsible for those materials. Interestingly, the template certification provided by the judge does not necessarily require an attorney to disclose whether they have used Generative AI-powered tools to prepare their legal submissions, only that, in case such tools were used, a human attorney has verified the submission and the attorney takes full responsibility for its contents. As such, the certification requirement is not very different from the already existing obligation on the attorney of record to diligently oversee that all submissions presented to the court are of the appropriate quality.
Meanwhile, the Court of King’s Bench in Manitoba, Canada, has adopted a more prescriptive disclosure practice, mandating that legal submissions presented to the court also provide disclosure of whether and how AI was used in their preparation. However, it does not mandate the disclosure of use of AI to generate work products often used to analyse cases, such as chronologies, lists of issues, and dramatis personae, upon which legal submissions may rely.
On the other hand, New Zealand and Dubai represent contrasting models of disclosure obligations. New Zealand’s guidelines for lawyers do not necessitate upfront disclosure of AI use in legal work. Rather, they focus on the lawyer’s responsibility to ensure accuracy and ethical compliance, and disclosure of specific use of AI-powered tools is required only upon direct inquiry by the court. This approach prioritises the self-regulation of legal practitioners while maintaining flexibility in how AI-powered tools are integrated into legal practice. In contrast, the Dubai International Financial Centre (DIFC) Courts recommend early disclosure of AI-generated content to both the court and opposing parties. Such proactive disclosure is viewed, in that context, as essential for effective case management and upholding the integrity of the judicial process.
On the other side of the bench, some jurisdictions have unveiled guidelines for using Generative AI-powered tools by courts and tribunals. New Zealand and the UK now provide frameworks for judges and judicial officers. These guidelines emphasise the importance of understanding Generative AI’s capabilities and limitations, upholding confidentiality, and verifying the accuracy of AI-generated information. In principle, neither jurisdiction’s guidelines require judges to disclose the use of AI in preparatory work for a judgment.
The drafting of legal submissions and arbitral awards are not the only areas where AI-powered tools may be integrated into an international disputes practice. AI-powered tools may also play a role in identifying and shortlisting arbitrators. This application carries potential implications for diversity and fairness in arbitrator selection. Typically, neither parties nor institutions must disclose their reasons for appointing particular arbitrators or the process they undertook to shortlist candidates. However, disclosure may be relevant where AI-powered tools are used to identify and potentially select arbitrators, given the biases and risks inherent in AI training tools and datasets.
Indeed, there are relevant parallels between the arbitrator selection process and general recruitment procesess, as both involve evaluating and selecting candidates for specific roles. Legislative steps, such as New York City Local Law 144 (New York Law 144), regulate the use of AI-powered tools in recruitment, highlighting the importance of transparency and accountability in AI-assisted candidate selection processes. New York Law 144 requires Automated Employment Decision Tools (AEDT) to undergo annual bias audits to ensure fairness and transparency. Similarly, the European Union’s concerns, as expressed by the Permanent Representatives Committee, underscore the need for careful regulation of AI in selection processes to protect individuals’ career prospects. While audits of AI databases and algorithms can help identify and rectify any inadvertent biases, completely eliminating diversity-related biases remains a significant challenge. For instance, Google’s recent efforts to subvert racial and gender stereotypes in its Gemini bot encountered backlash, illustrating the complexity of addressing biases without introducing new issues.
Integrating Generative AI-powered tools into the work of litigation and arbitration teams has prompted new conversations on regulatory measures, including disclosure and certification requirements, to ensure their ethical and fair application. The SVAMC Draft Guidelines, the AAA-ICDR Principles, and the MIT Principles each present exemplar frameworks for the responsible use of Generative AI-powered tools, emphasising transparency, accountability, and ethical standards. Moreover, various jurisdictions have adopted different approaches to disclosure or certification requirements, thereby demonstrating a range of policy-driven priorities. However, collectively, these recent developments signal a critical juncture in the legal profession’s engagement with Generative AI, stressing the need for adaptable, forward-looking regulatory frameworks that uphold the integrity and fairness of legal processes.
*The opinions and insights presented in this post solely represent the authors’ views. They are not endorsed by or reflective of the policies or positions of their affiliated firms or organisations.
The strategic implications of using tech at an earlier stage in the litigation lifecycle is really starting to land with legal teams. We explore the reasons behind this trend; as you may have already guessed, AI may just be the catalyst. With huge amounts of digital data to navigate, we also ask whether practitioners will actually have a choice in the future.
AI-Powered Semantic Search
The traditional challenges of sifting through vast amounts of legal documents to find relevant information are significantly alleviated by AI-driven semantic search. With platforms like TrialView, the capacity to understand context, meaning, and relationships between words, pinpointing critical details efficiently takes seconds. From pleadings to evidence, AI-enhanced semantic search transforms the way we manage information, ensuring not only speed but also a higher level of precision in document retrieval.
Seamless Collaboration
Collaboration lies at the heart of dispute management, and AI amplifies this aspect in the realm of electronic bundling. Cloud-based solutions, like TrialView, when infused with AI capabilities, facilitate seamless collaboration among legal teams. Multiple professionals can work simultaneously on shared documents, with AI algorithms suggesting improvements and ensuring consistency. The result is a more cohesive workflow, breaking down silos and enhancing the collective intelligence of the legal team.
Persuasion
Flowing through from early case management to the courtroom, AI tools offers a real opportunity to enhance the overall impact during a hearing. The art of persuasion meets the precision of data, which can be interrogated and presented in an effective and visual way to the court or tribunal.
Cost Efficiency
The integration of AI, used at a much earlier stage, has the potential to significantly reduce costs. Aside from the evident saving on printing, AI mitigates hours spent on the mundane, from disclosure through to bundling. Moreover, AI pervades the entire data set, opening up the potential to find and track patterns throughout the data.
The Future
The early adoption of platforms, like TrialView, enhanced by AI’s capabilities in semantic search and timeline creation, is not just a tech trend, but represents a fundamental shift in the way legal professionals approach disputes.
Ultimately, it’s an investment in efficiency, collaboration, and strategic case management — a commitment to staying at the forefront of a shifting disputes landscape.
