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?
Richard Pike
Partner
COMPETITION | REGULATION AND DISTRIBUTION LAW | DISPUTE RESOLUTION | PUBLIC AND REGULATORY | TECHNOLOGY
Tel: +44 7986 244995
Electronic ways of working in litigation feels very much like the norm, but the hidden benefits of using workspaces and digital tools are starting to come to the fore.
Features such as search, tagging, automatic date detection and indexing are essential components in any bundling tool, expediting the process and saving cost, but what hidden benefits are often overlooked and why should you consider going digital for hearing prep?
Search Functionality
Efficient Retrieval: Search functionality allows users to quickly locate specific documents within a large electronic bundle. Essential for time-sensitive tasks during litigation, having the capacity to find a fact or a document in seconds is indispensable.
Less manual, more time: Searching electronically eliminates the need to manually sift through extensive document sets, saving valuable time and reducing the risk of oversights. Your client will thank you, on both counts.
Comprehensive Analysis: Analysis is simply more efficient. Searching for specific terms, phrases, or keywords facilitates identification of patterns and trends in the data set. Not so easy with boxes of paper files.
Tagging
Structure: Tagging documents with relevant keywords or categories enhances the organisational structure of the electronic bundle, ultimately making it easier for legal professionals to categorize and retrieve documents efficiently.
Customisation: Tags can be customized based on the unique needs of a case, facilitating a tailored system which aligns with the nuances and complexities of the litigation.
Collaboration: Tagging promotes collaboration within legal teams by providing a standardised method for categorizing and referencing documents, fostering consistency and clarity in communication. Streamlining is the end goal.
Dates
Chronology: Organising documents by date is critical for presenting a chronological sequence of events, augmenting the understand of the timeline of the case, identifying key milestones, and helpful in establishing causation.
Timeline Building: Dates are essential for constructing a timeline of events, which is particularly valuable in litigation for presenting a clear and compelling narrative to the court.
Compliance: Adherence to court-imposed deadlines and legal procedures is facilitated by accurate date information. We all know that failure to meet deadlines carries serious implications.
Features that may sound trivial can have a huge impact on the effective management and analysis of electronic bundles in litigation. These features not only streamline document retrieval and organisation but also enhance collaboration, compliance, and the overall strategic approach to legal proceedings.
Why not join other legal teams and go digital with TrialView?
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.
Jason Alvares
Senior Forensic Technology Manager
Business and Commercial Disputes
Tel: +44 (0) 20 3400 3272
India Fahy
Associate
Antitrust and Competition
Tel: +44 (0) 20 3400 2250
Edward Coulson
Partner
Antitrust and Competition
Tel: +44 (0) 20 3400 4968
How can litigators think creatively and independently in the world of artificial intelligence and data-driven prediction?
Join Solomonic and TrialView on Friday, 8th December at 9am for a virtual panel discussion on the Future of the Courtroom: Embracing AI & data-driven prediction in litigation.
Our expert panel includes Senior Counsel and our TrialView founder Stephen Dowling SC, HFW partner Brian Perrott, and Pinsent Masons legal director Christopher Dryland, who will discuss the evolving role of the litigator, and the opportunities to leverage data and AI. This is going to be a powerhouse panel discussion that will explore how a creative mindset will ultimately empower litigators to adapt and thrive.
Register your place here. We hope you can join us.
The Guidelines seek to establish a set of general principle for the use of AI in arbitration. They are intended to guide rather than dictate. They do not intend to replace or override local AI laws of regulations.
The SVAMC Guidelines split into three chapters:
1) Guidelines applicable to all participants in international arbitration
2) Guidelines for parties and party representatives
3) Guidelines for arbitrators
Chapter 1
Guideline 1: Understanding the uses, limitations and risks of AI applications
The AI tool’s terms of use and data handling policies need to be reviewed by participants in order to understand if the tool’s data treatment is consistent with applicable confidentiality, privacy or data security obligations.
Participants should make reasonable efforts to understand the functionality, limitations and risks of the AI tools used in preparation for or during the course of an arbitration proceeding. This includes the following:
“Black-box” problem: Text produced by Generative AI is a product of complex probabilistic calculations rather than intelligible “reasoning”. AI lacks ability to explain their own algorithms. Where possible, participants should therefore use AI tools and applications that allow them to understand how a particular output was generated (“Explainable AI”).
AI tools may not be well-suited for tasks requiring specialised knowledge or case-specific information unless they are fine-tuned or provided with more relevant data.
Errors or “hallucinations”: This will occur when AI lacks information to provide an accurate response to a particular query. Errors can be reduced through “prompt engineering” and “retrieval-augmented generation”.
Augmentation of biases: biases may occur when the underrepresentation of certain groups of individuals is carried over to the training data used by the AI tool to make selections or assessments. Participants are urged to exercise extreme caution when using AI tools for this purpose.
Compliant: Using AI to conduct research on potential arbitrators or experts for a case Non-Compliant: Using it to select arbitrators or experts for a case without human input
Guideline 2: Safeguarding confidentiality
Need to ensure use of AI tools is consistent with obligations to safeguard confidential information. Confidential information should not be submitted to any AI tools without appropriate vetting and authorisation.
Participants should review the date use and retention policies offered by the relevant AI tools.
Compliant: Using AI for routine non-confidential tasks e.g., meeting scheduling or to research/summarise legal authorities in a third-party database. Non-Compliant: Submitting confidential information to a third-party AI tool as described above.
Guideline 3: Disclosure and protection of records
Some uses of AI by parties, experts and arbitrators may be uncontroversial and would not ordinarily warrant disclosure. There are certain circumstances where disclosing the use of AI tools may be warranted to preserve the integrity of the proceedings or the evidence.
A party seeking disclosure from another party should explain both why it believes that an AI tool was relied upon in the proceedings and how it would materially impact the proceedings and/or their outcome.
It is ultimately up to the parties and/or tribunal to specify the level of disclosure they want to institute for the proceedings.
Compliant: Using AI to generate document summaries for internal use or to identify and select the documents relevant and responsive to document production requests. Non-Compliant: Using AI to calculate damages without disclosing it. For an arbitrator to use AI to compare persuasiveness of parties’ submissions without disclosing it.
Chapter 2
Guideline 4: Duty of competence or diligence in the use of AI
Parties and party representatives on record shall be deemed responsible for any uncorrected errors or inaccuracies in any output produced by an AI tool they use in an arbitration.
Compliant: Using AI to assist with drafting language for pleadings/written submissions or to assist in preparation of XX or find inconsistencies in witness statements.
Guideline 5: Respect for the integrity of the proceedings and the evidence
Parties, party representatives and experts shall not use any form of AI to falsify evidence, compromise the authenticity of evidence or otherwise mislead the arbitral tribunal and/or opposing party(ies).
Advancements in Generative AI and deep fakes can heighten the risks of manipulated or false evidence and can make it more costly or difficult to detect any such manipulation through forensic and other means.
Compliant: Using AI to produce demonstratives where the accuracy of the representation can be challenged by the opposing party by accessing the referenced source data.
Chapter 3
Guideline 6: Non-delegation of decision-making responsibilities
An arbitrator shall not delegate any part of their personal mandate to any AI tool.
This Guideline does not forbid the use of AI tools by arbitrators as an aid to discharge their duty to personally analyse the facts, arguments, evidence and the law and issue a reasoned decision.
If an arbitrator used a Generative AI tool to assist in the analysis of the arguments or the drafting of a decision or award, the arbitrator cannot reproduce the AI’s output without making sure it adequately reflects the arbitrator’s personal and independent analysis of the issues and evidence at hand.
Compliant: Using AI to provide accurate summaries and citations to create a first draft of the procedural history of a case or generate timelines of key facts.
Guideline 7: Respect for due process
An arbitrator shall not rely on AI-generated information outside the record without making appropriate disclosure to the parties and allowing the parties to comment on it.
Where an AI tool cannot cite sources than can be independently verified, an arbitrator shall not assume that such sources exist or are characterised accurately by the AI tool.
Compliant: Using AI to distil or simplify technical concepts to come up with accurate and relevant questions for the hearing. Non-Compliant: Using AI to conduct independent research into substance of the dispute and base decision on such generated outputs without disclosing it to the parties.
