Author: aleks

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.

Managing and Producing Documents in the Age of Big Data

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.

End-to-end ODR Platforms

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

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.

AI-enabled Machine Translations

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.

Conflict Management and Arbitrator Diligence

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.

AI-driven Data Analytics for Third-Party Disputes Funding

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.

Is There Still Room to Break Ground?

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.

Concluding Thoughts

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.

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.

The Need to Regulate the Use of Generative AI-Powered Tools in Dispute Resolution Practice

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.

Guidelines and Principles for Legal Practitioners

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.

Regulation, Disclosure, or Self Policing?

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.

Potential Regulation of AI Use in Arbitrator Identification and Selection

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.

Conclusion

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.

In “Better Together: Creative Case Management by the CAT”, Edward Coulson, Lindsay Johnson and India Fahy explored a number of creative approaches to case management that have been adopted by the UK’s specialist Competition Appeal Tribunal (‘CAT’) in seeking to grapple with the explosion of the number of damages actions before it.

In this guest post for TrialView, the team at BCLP look at how case management by the CAT is fast-developing and far from settled in its approach.

INTRODUCTION

As the CAT seeks to grapple with the challenge of resolving multiple cases relating to the same infringement, almost invariably including a flurry of individual claims, as well as collective actions, it has become increasingly apparent that, when it comes to disclosure, no one size fits all and the CAT is also feeling its way to the correct approach to managing disclosure in complex damages actions.

In this article, we explore a number of different approaches to disclosure recently adopted by the CAT and how advancements in technology may be leveraged by the CAT to overcome disclosure challenges.

APPROACHES TO DISCLOSURE

Competition damages actions often involve very complex disclosure issues, as a result of factors such as the number of parties and issues involved, the historic nature of the conduct concerned, and the availability of documentary evidence and data. Such cases typically involve extremely voluminous disclosure and concerns have been expressed by the CAT and by parties on all sides about the disproportionate cost of providing and reviewing disclosure.

It is fair to say that the CAT has seen a need for close case management in such cases and has taken a ‘hands-on’ approach to managing disclosure. In the CAT’s Disclosure Ruling in the ‘First Wave’ of Trucks , it was explained that the CAT will tailor disclosure orders to what is proportionate in each individual case and disclosure in a damages claim such as Trucks requires close case management by the CAT. The Disclosure Ruling outlined certain broad principles that are applied by the CAT, including for example that disclosure will only be ordered if it is limited to what is reasonably necessary and proportionate bearing in mind a number of aspects of the particular action. It is evident from the evolution of different approaches to disclosure that the courts are willing to explore all options for resolving cases efficiently and reducing the enormous cost of disclosure.

The range of options being explored in competition cases can perhaps be best observed by looking at the differences between the approach of Smith J, President of the CAT, in Genius Sports in the High Court, which the President has described as a regime of “over-inclusive” disclosure, and the approach currently being trialled in the CAT, which the President has described as “a non-disclosure-based process”.

Genius Sports

In Genius Sports, Smith J ordered a bespoke regime involving an “over-inclusive” approach to disclosure of documents between the parties, with only “unequivocally irrelevant and privileged documents” to be excluded from the disclosure exercise, leaving the receiving party to review the documents itself. Smith J made this order on the basis that there was a risk that under a standard approach, relevant documents would be omitted, and on the basis that “massive over-disclosure” “no longer gives rise to the “real risk that the really important documents will get overlooked.. rather, the electronic filtering of documents gives rise to the real risk that really important documents are not looked at by any human agent”.

Smith J’s explanation of the process can be summarised as follows:

• Each party would identify to the other precisely what documents would be subject to an electronic search and would swear an affidavit identifying custodians, repositories and collections of documents to be searched, together with any date ranges that would be applied to exclude or include material;
• In defining the universe of documents to be searched, each Producing Party should err on the side of over-inclusion;
• The object of the electronic review is to filter out documents that are irrelevant on the Peruvian Guano test, not to identify relevant documents;
• Each Receiving Party should be fully informed as to the nature of the electronic review that has been conducted;
• At the conclusion of each party’s review, there will be a corpus of documents that exclude the unequivocally irrelevant and a further review to filter the documents further on grounds of relevance should not take place;
• In order to identify privileged material, to be excluded from disclosure, Smith J indicated that whilst an “eyeball” review would be best, it’s unlikely to be feasible and accordingly what is likely to be appropriate is an electronic search targeted specifically at the identification of privileged material, which is then reviewed by a human agent.
• There should be no filtering on grounds of confidentiality – confidential material must be produced to the Receiving Party. Smith J indicated that confidential material would be protected in a number of ways, beyond CPR 31.22 (the rule applicable in High Court proceedings that prohibits the party receiving disclosure from making collateral use of it ), including, for example, through the use of auditable access to disclosure platforms, with parties obliged to keep a record of who accesses what document and when.

In a recent speech, Smith J reflected on this approach and remarked that:

“The point is that whilst everybody trusts an “eyeball” review by a professional and regulated team, no-one trusts the other side’s electronic search processes. And for good reason: the algorithmic “black boxes” that exist now (AI; concept grouping; etc. – keywords are so passé!) are robust in different ways in terms of the generosity or otherwise of their relevant document production, and the party receiving disclosure is entitled to understand how well the process has worked.”

In the particular case before him, Smith J concluded that the solution, which “is not a one size solution – and some would say it is not a solution at all”, was the receiving party being permitted to run the process themselves and carry out whatever searches they wish, understanding that excessive costs would not be recoverable.

“Non-disclosure-based process”

Smith J has explained the impetus for what he terms the ‘non-disclosure-based process’ as being that, in competition cases, “what we want is data without the disclosure. Probably collated by experts, from materials held by the parties, but using a non-disclosure-based process”.

Such an approach has recently been adopted in a number of cases before the CAT, including in the Boyle v Govia and McLaren v MOL collective actions and Smith J has suggested that this approach, which is being trialled in the CAT, could be rolled out more broadly. In both cases, Smith J expressed significant doubt about the efficiency and proportionality of traditional disclosure exercises in collective actions and instead stressed the importance of an expert-led approach, with experts being provided with “data and information, not reams of documents (whether paper or electronic) that must be sifted and analysed and turned into usable data and information”. In such a case, Smith J explained that one party’s expert should articulate the need for data or information, and for that data or information to be produced by the expert on the other side. The CAT recognised that it may be necessary for an ‘audit’ to be called for but, in the first instance, data should be regarded as reliable. Such an approach does not exclude the disclosure of documents but rather shifts the focus to disclosure of data and information, through an expert-led process.

From an order for “massive over-disclosure” in Genius Sports to trialling a “non-disclosure-based process” in the CAT would seem to be quite a jump but it is apparent that Smith J is prepared to explore a range of options to identify the most efficient approach in a given case. For example, both Boyle v Govia and McLaren v MOL are collective actions, which makes claimant side disclosure particularly challenging and justifies a different approach to disclosure being employed.

THE EXPANDING ROLE OF TECHNOLOGY

The options at both ends of the spectrum raise very significant considerations for parties to competition litigation. In the case of “massive over-disclosure”, parties will be concerned to ensure that, for example, privileged material is protected from disclosure, and in the case of “non-disclosure”, parties will be concerned to ensure that the process of selection and production of the data is transparent and robust.

Technology of course already plays an integral role in disclosure in almost all major litigation before the UK courts but how may developments in artificial intelligence and machine-learning be leveraged to assist the CAT in grappling with the challenges before it and the parties in managing the approaches to disclosure ordered?

Predictive coding, a form of artificial intelligence (‘AI’) tool, emerged in the early 2010s and our firm, BCLP, was successful in the first contested application before the High Court for disclosure to be carried out using predictive coding in 2016.

In the years since, there has been a rapid acceleration of uptake in the acceptance and use of AI and data analytics in litigation and significant growth in data volumes and evolution of data types. As we have seen data volumes grow and types evolve, there has also been a spike in the number of bespoke software applications being created to try and tackle challenges faced by parties in litigation, as well as refinement and improvement of current feature-rich tools and algorithms to improve the enrichment and throughput of the results. For example, we have seen the use of Continuous Active Learning (known as CAL) assist in managing the review of extremely voluminous datasets in a defensible manner. CAL is a process of active learning, where key and relevant documents are prioritised by the technology for further review by human agents, with documents that are similar to those that have been coded by human agents as not relevant being de-prioritised.

As the parties grapple with challenges posed by creative disclosure case management approaches ordered by the courts, parties will likely turn to the wide range of technologies for cost-effective and proportionate solutions. For example, a party concerned about protecting privileged information from disclosure under an ‘over-inclusive’ disclosure process will not be reliant on more traditional options, such as keyword searches, and can instead explore options such as pattern recognition and concept grouping to assist with the process. Concept grouping is a very powerful tool when it comes to identifying documents which are likely to either be relevant or not relevant.

At the other end of the spectrum, parties grappling with the challenge of the ‘non-disclosure-based process’ will similarly need to find new ways of ensuring that the data they receive has been robustly selected and produced. In McLaren v MOL , the President suggested that the parties may wish to consider the use of a “data consultant… Someone – an organisation that is retained by all of the parties to assist in the synthesis of data”, to ensure that data has been properly produced and is reliable. As parties, and the courts, become increasingly reliant on technology and data, might we ever reach a point when it is necessary or desirable for the Tribunal to have, as it often has an expert economist on the panel, a data expert on its panel?

Until recently, when people have referred to the use of AI in disclosure, such references have typically been to the use of tools such as predictive coding and CAL. However, it is inevitable that this is only the beginning. Companies are increasingly storing data in a manner which results in larger, unstructured, datasets. This presents challenges for human agents seeking to conduct cost effective reviews of disclosure, without the assistance of AI tools to sort and interpret the data.

This is particularly so, as Smith J has alluded to, in competition cases where millions of datapoints are used to model overcharges and rates of pass-on, for example. As the CAT becomes increasingly focused on data, it is inevitable that parties will need to turn to increasingly advanced new forms of AI to increase the efficiency and efficacy of disclosure reviews.

AI already has and will continue to change the way in which parties approach disclosure in complex damages actions and will be an important tool for the CAT in seeking to alleviate costs issues, whilst preserving the integrity of the disclosure process. It is not inconceivable, for example, that cognitive computing evolves to the point at which the CAT may itself ask a form of AI technology a natural language question about what the data shows or what data is most relevant to a particular issue.

As technologies develop, we may see an increased need for data experts and data consultants to assist both the parties and the CAT with adjusting to new ways of approaching disclosure issues.

A team from European law firm, Fieldfisher, renowned for its innovative approach, worked with TrialView to navigate a complex case before the Competition Appeal Tribunal. The case in question was highlighted by The Lawyer as one of its “Top 20 cases for 2023” and led by the highly regarded regulatory and competition litigation Partner Richard Pike, and a team comprising Director Simon Yeung, Associates Agnieszka Szewczyk and Alicja Dijakiewicz and Senior Paralegal Andreas Killi, amongst others.

The Challenge

The nature of the case demanded the creation of confidentiality rings, i.e., restricted access to confidential information by specific individuals or groups. Ensuring the security of the confidential data was paramount, with manual methods of managing confidentiality simply untenable due to the scale and complexity, as well as the pace, of this case.

The Solution

The Fieldfisher team partnered with TrialView and the Defendants’ lawyers to tailor the TrialView software to facilitate compliance with the Tribunal’s confidentiality order, whilst providing access to all registered users in a single, combined workspace.

How did this look in practice?

• With robust user authentication protocols, only authorised individuals had access to the confidential information. Two-factor authentication and role-based access control were key components.The most confidential information was only visible to parties within a court ordered inner confidentiality ring, while less sensitive but still confidential material was visible to those in an outer confidentiality ring. Users who were not members of the inner or outer confidentiality rings were able to view redacted versions of confidential material.
• Permission settings on TrialView facilitated the designation of users into teams and specific roles, with each role aligned to their individual membership of the confidentiality rings.
• Special controls ensured that a user would only have access to the version of the document they were entitled to see.
• Evidence presentation was designed so that different users could see different versions of the same document simultaneously (based on the log in credentials).
• Version control facilitated consistency of pagination, structure and tabbing.
• Dynamic watermarking ensured that exports and bundles contained the correct confidentiality designation, ensuring that any shared document could be traced back to source.
• Exports and bundles of documents could also be set to align to the recipient’s confidentiality ring membership.

What were the results?

The additional confidentiality ring functionality significantly enhanced the useability of the database, minimising concerns around disclosure of confidential information to those without the necessary rights to view such information and complying with users’ confidentiality undertakings and the Tribunal’s confidentiality order. Having the different confidential variants in one workspace made working and updating the trial bundle easier.

Fieldfisher Director, Simon Yeung, commented on his experience with TrialView:

“TrialView has an intuitive interface and was straightforward to use. The support from the team at TrialView was very responsive, including the implementation of the confidentiality ring functionality.”

Fieldfisher is one of a leading number of visionary firms who recognise the efficiency gains from embracing innovation and technology at an early stage in a dispute.

Why not get in touch with one of our consultants to find out more?

eBundling provides several benefits and allows legal professionals to expedite the bundling process and work with enhanced speed and efficiency.

However, navigating new court guidelines can still be challenging, which is why we have created a refresher on eBundling which provides a useful overview on the core requirements to help you get ready for trial.

This information is for educational purposes only; for a full overview of the Commercial Court requirements, please click here.

What is a court bundle?

Firstly, what is a court bundle? Simply, a court bundle is no more or less than a collection of documents which are considered to be relevant to a case.

A core bundle should contain only the most important documents which are to be relied upon in the case.

Key points to consider when creating a court bundle

Courts now require only electronic bundles to be filed, unless hard-copy bundles are requested by the Judge specifically.

Co-operation between the parties is a duty required by all legal representatives when it comes to bundling.

Your up-to-date guide to eBundling

1. Bundle Formats

All hearing bundles (save for the core bundle) should be combined into one .pdf file. The file name for each .pdf should contain: the Claim No., a short version of the case name, and an indication of the number/letter of the bundle – e.g. “Bundle A (CL-2022-000111), Smith v Bloggs”.

All significant documents should be bookmarked, and should contain the page number of the document.

Individual bundles should be indexed, and the index should be searchable. A separate index or table of contents should be provided, and indexed documents should be hyperlinked. No more than one copy of each document should be provided, unless there is a justifiable reason.

2. Cross Referencing

Cross-referencing is central and prescriptive to the new guideline changes; it aids the Judge in quickly being able to understand the relationship between documents, particularly where specific exhibits or witness statements mention or rely on specific documents.

Hyperlinking is an easy and routine way to cross-reference across your bundle.

3. Core Bundle Requirements

Each page should bear its main bundle and page number reference and should be separately paginated. It must also be in PDF format.

Additionally, bundles must be lodged with the Court by 4pm on the working day before the first day or the trial or hearing.

4. General Guidance

Large electronic files can be slow to transmit and unwieldy to use and therefore should be avoided where possible. Therefore, bundles should only contain documents and authorities which are necessary for the hearing.

Additionally, each bundle needs to be named concisely, identifying its type.

5. Document Formats

Documents must be subject to OCR and should appear in portrait mode, save for bundles which contain only landscape-format documents, such as spreadsheets. Metadata should also be removed.

Download the full guide to commercial court bundling guidelines today to discover more about the how your court bundles should be presented.

Make complex court bundling easy with TrialView

TrialView offers one platform for great results, enabling fast and efficient bundle creation and allowing you to work with speed and efficiency for every aspect of your case.

Our award-winning solutions for e-bundling, case preparation, and evidence presentation is powered by AI technology and trusted by law firms, courts, and tribunals across the world.

If you’d like to find out more about the benefits of TrialView and how it can help ensure court compliance, get in touch today to learn more about our court bundling software.

Alternatively, book a tailored demo here.

The Rise of the Litigation Workspace

Litigation is undergoing a quiet revolution. Underpinning these changes are powerful AI and ChatGPT tools, which are ultimately changing how we work, and impacting on outcomes. The days of manual processes are numbered. Digital tools are now commonplace in litigation. But the next phase of development will see a harnessing of these tools to bring efficiencies never thought possible – with implications for those who do not adapt. .