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Artificial Intelligence: Implications for Litigation Investigations, and Dispute
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September 17, 2024
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Generative artificial intelligence is gaining increasing traction within numerous functions across the legal profession. Hype and AI’s challenges, from bias to transparency and explainability, aside, it’s clear that AI has the potential to revolutionize the legal industry. There are numerous opportunities to enhance efficiency, accuracy, and strategic decision-making in areas including litigation, investigations, and disputes.
Signs of Rapid AI Adoption
With AI, change is fast. Six months after OpenAI launched its first version of ChatGPT, a Thomson Reuters Institute survey found that only 3% of attorneys and other law firm personnel surveyed said their firm was currently using AI – and only one-third attested that their firm was considering it. Surprisingly, at least in retrospect, 60% said their firm had no current plans for using AI.1
Just nine months later, it was clear that AI was already transforming the practice of law in the United States, largely due to the intuitive nature of large language models and their easy-to-use interfaces. A survey of every Am Law 100 law firm, discovered that while many of these leaders limited early use of generative AI to tasks that didn’t require or involve client data, 41 were actively using AI to advance priorities like exploring legal opportunities and improving their business and administrative operations.2
AI in Litigation: From Trial Preparation to Automated Document Generation
Today, legal professionals are looking to expand their use of AI and, just a few short months since the Am Law survey, are now cautiously experimenting with advanced technology.
Electronic Discovery and Document Review
AI, specifically the use of document classification techniques, has been in wide use for a decade or more in the e-discovery context. Whereas legions of attorneys once manually reviewed all documents in the scope of a matter, a classification process such as predictive coding or technology assisted review can be applied to documents to drastically reduce the population of in-scope data for legal analysis.3 This and other AI applications have greatly mitigated both the monetary and temporal costs of litigation and, in many instances, improved e-discovery work product despite the staggering growth and complexity of electronic data. When applied to the right workflows, AI also enhances traditional natural language processing, structuring of data, filtering irrelevant content, and identifying critical insights early in a case.
Predictive Coding and Technology Assisted Review
Text Classification is a machine learning task for assigning one or more categorical labels to an instance of text where the user already knows something about the data. This workflow translates well to categorizing legal issues across a set of documents based on an attorney’s legal subject matter expertise and firms have found significant success in reducing e-discovery time and cost by using supervised learning techniques such as predictive coding and/or technology assisted review where the subject matter expert iteratively trains a “machine” to understand written or spoken language as though it were itself the expert. Once decisions between user and machine stabilize to an acceptable rate, the resulting model is harnessed to apply issue labels to all of the document set.
AI-Driven Fact Finding
Legal technology users have traditionally leveraged clustering and classification for categorizing documents, sacrificing context for the efficiency gains provided in filtered results. Combining the right tools can assist with defensibly organizing findings, e.g., chronology creation, prioritizing data for review, and expediting fact finding that bolsters client claims, defenses and general data awareness around complex litigation.
Contract Solutions and Automated Legal Document Generation
AI-enabled contract solutions similarly help organize contracts and extract clauses for analysis and modeling. When combined with expert feedback, AI effectively supports automation for certain steps of drafting contracts, motions, pleadings and other legal documents. Tools can use templates customized to various legal situations as well as a defined set of rules to reduce manual work in document creation. This has the potential to save time and cost, and with expert oversight, can align to legal requirements, industry standards, and internal policies and working practices.
AI in Investigations: From Fraud to Case Management
AI can support attorneys with understanding the truth of a given matter and determining what happened.
Fraud Detection and Prevention
AI-driven algorithms and other tools can be powerful for detecting, preventing, and litigating several types of fraud within organizations. Fraudulent financial transactions, for example, can be unearthed via AI capabilities such as anomaly detection (e.g., unusual transfers or changes in spending patterns), predictive modeling (e.g., projections based on historical data), and real-time monitoring (e.g., scanning for suspicious activity.) Investigating money laundering can be greatly enhanced via network analysis, identification of entities involved in suspicious transactions, and natural language processing that analyzes textual data. AI also helps identify other types of fraudulent activities, such as insider trading, accounting fraud, and intellectual property theft.
The Value of Augmented Investigations™
At FTI Consulting, teams focused on assisting clients with litigation, investigations, and disputes as well as advanced data and analytics have developed a proprietary capability we refer to as Augmented Investigations™.
This approach leverages advanced analytics to handle complex, data-heavy investigations that provide a comprehensive and holistic review of organizational data, helping clients manage risk, ensure compliance, and resolve disputes effectively.
Digital Forensics and E-Discovery
AI-powered tools can support analysis of artifacts like texts and emails, reports and other documents, and multimedia files, identifying unseen patterns, anomalies, and items of interest warranting further examination.
Investigative Data Analysis
When data sources are especially vast, AI can examine these at speed to identify patterns that may be difficult to detect. This helps investigators on case teams to craft more insightful hypotheses, rank leads, and make data-driven decisions.
Case Management and Workflow Optimization
On the administrative side, attorneys traditionally lean heavily on personnel and manual tasking to manage complex cases simultaneously. AI has proven valuable in taking over routine tasks. It also helps eliminate bottlenecks, make processes more efficient, and improve collaboration across teams.
AI in Disputes: From ADR to Trial Presentation
AI is also impacting how attorneys address disagreements between counterparties – from client consultation and intake, investigation and evidence collection, pleadings and court documents, and ultimately negotiations, settlement, or trial representation.
Alternative Dispute Resolution (“ADR”) and Negotiation Support
In ADR, AI has the potential to power intelligent negotiation support systems. It can also evaluate historical negotiations, identify tactics such as problem framing, targeted outcome framing, bluffing, and making concessions. When applied to the right use cases and led by experts, AI-driven tools can also streamline mediation and contribute to fair and equitable settlements more likely to be accepted by both parties.
AI can leverage historical case data, pattern and trend analysis, probability theory and other inputs. Additionally, it can also shine a spotlight on critical factors such as jurisdictional characteristics, the breadth and strength of evidence, the scope and nature of claims, witness credibility, and the experience of legal teams and their individual members.
Expert Witness Analysis
Another area of law AI is reshaping is how witnesses are managed. AI-based tools can help identify the experts with the most appropriate credentials, backgrounds, and expertise – and then evaluate their credibility by reviewing their past court testimony, publications, and online profile.
Trial Preparation and Presentation
In addition to the areas above, AI can help attorneys prepare witnesses and create compelling presentations and graphics that communicate important evidence to the judge and jury, by analyzing inputs from documents to videos and audio recordings and using this information to help attorneys make stronger data-informed decisions. Another contribution is real-time feedback on the impact of the trial presentation as it unfolds – and in the broader, data-rich context of the dispute – suggestions on adaptations or changes in approach that may result in a more successful outcome for either the client or both counter parties.
AI in the Legal Profession: Challenges and Special Issues
In-house attorneys and their law firms face several complex issues that complicate the path to full adoption. Keep in mind that these issues extend beyond the walls of the organization to its vendors and business partners.
- Ethical concerns and bias – AI algorithms can perpetuate bias and result in discriminatory outcomes. As a result, safe use of the technology requires improving accountability for its design and use (e.g., human-in-the-loop) and ensuring that the technology is, at the appropriate level, transparent and “explainable.”
- Data security and privacy – AI can inadvertently compromise sensitive, proprietary, or restricted client information. Misuse may raise the risks of data breaches or cybersecurity incidents – and result in violations of strict data privacy regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). Consequently, data security measures such as encryption, access controls, security audits, employee training, and incident response plans must be implemented across AI deployments.
- Regulatory compliance – The legal ramifications of AI are still emerging, and the regulatory environment unclear. This makes it difficult to anticipate where the legal guidelines will be on use of AI in various applications – particularly since these are likely to vary across jurisdictions and authorities.
- Technical challenges – Use of AI requires integrating it into existing platforms, applications, and capabilities – and then redesigning processes and retraining personnel on its use. Doing so, in turn, will place new demands on expertise, investment, and planning.
Additional challenges include areas such as HR and training (e.g., job displacement, specialty talent recruitment, skill development) and return-on-investment (e.g., initial investment, ongoing maintenance costs)
Looking Ahead: AI is Here to Stay
The legal profession is well versed in adapting to technological change. Think back to how laborious it was to handle discovery before the advent of electronic discovery technology – or legal research before the emergence of massive legal research databases such as Westlaw and LexisNexis. Consider the game-changing benefits of new capabilities like document management systems and cloud computing.
While the hype can lead to misconceptions, AI will change aspects of the legal profession – especially for litigation, investigations, and disputes. Counsel must understand what this will mean for their legal departments, their most complex cases, their law firms’ workflows, their competitors, industry and more.
While the full strategic significance of AI and its capabilities is still taking shape, legal teams taking a targeted and sophisticated approach to embracing AI innovation in ways that are reliable, secure, fair, transparent, and accountable will set the bar for the profession for decades to come.
Footnotes:
1: “ChatGPT and Generative AI within Law Firms,” Thompson Reuters Institute (TRI) (April 2023).
2: Justin Henry, “We Asked Every Am Law 100 Law Firm How They're Using Gen AI. Here's What We Learned,” Law.com, January 29, 2024.
3: “Technology-Assisted Review in E-Discovery: A Practical Guide,” The National Law Review (2024).
Published
September 17, 2024
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