Jon Fowler, managing director of data solutions at Secretariat
The eDiscovery ecosystem, once dominated by manual document reviews and endless paperwork, has undergone a dramatic transformation due to artificial intelligence (AI). Generative AI, a particularly innovative branch that creates entirely new text, emails, or code based on existing data, offers immense potential yet faces complex challenges. By reflecting on the adoption journey of predictive coding, the first major AI tool in document review, we can glean valuable lessons for integrating generative AI responsibly and effectively.
Predictive Coding: A Catalyst for Change
Before predictive coding, sifting through mountains of documents for relevant information was a labour-intensive, time-consuming, and costly process. Predictive coding introduced a paradigm shift, empowering users to train AI models to filter and rank documents based on predefined relevance criteria. This resulted in:
Advertisement
- Increased Efficiency: Reviewers focused on a smaller, pre-classified pool of documents, significantly reducing time and cost.
- Improved Accuracy: Over time, trained models learned to identify relevant documents more accurately than manual methods.
- Enhanced Scalability: Large and complex data sets became manageable through AI-powered triage.
However, the adoption of predictive coding had its challenges. Many lawyers expressed concerns that AI would replace human judgment and the nuanced decision-making skills they had honed through years of legal training. Concerns regarding its scientific validity, transparency, and potential biases also sparked heated debates and legal challenges.
To aide adoption, it was crucial to emphasize that these tools, while adept at pattern matching and identifying relevant information, lacked the critical thinking, legal expertise, and client-focused communication that are the hallmarks of a skilled lawyer. The ecosystem responded by developing and refining best practices and standards, ultimately securing predictive coding’s place as a valuable tool. With these advancements, predictive coding illustrates how AI is a powerful tool to augment lawyers’ capabilities, freeing them to focus on the high-value, strategic tasks that only qualified lawyers can perform.
Parallels in the Generative AI Narrative
Similar concerns surround the potential impact of generative AI on the legal profession. However, it’s essential to understand that, just like predictive coding, generative AI will not replace lawyers. While offering advanced capabilities, these tools still need the crucial human element in legal practice. In essence, generative AI acts as a powerful asset, streamlining workflows and allowing lawyers to focus on the areas where their expertise truly shines, such as strategic planning and client interaction.
Generative AI: Unlocking Untapped Potential, Igniting Ethical Questions
Let’s shift our focus to generative AI and its potential applications in eDiscovery are vast and hold immense promise:
- Automated Document Creation: Imagine AI generating summaries of critical documents, drafting interrogatories, or even responding to legal requests, streamlining workflows, and boosting productivity.
- Enhanced Review Power: Generative models could identify hidden patterns and connections within data, aiding in relevance assessments, risk identification, and uncovering previously unseen insights.
- Personalized Legal Solutions: Tailored legal arguments or reports based on specific case nuances could be generated, expediting decision-making, and providing a more nuanced perspective.
However, alongside its potential, generative AI raises concerns that cannot be ignored:
- Hallucinations from a Search Perspective: Generative AI tools integrated into eDiscovery platforms raise concerns about “hallucinations” in their search results. These concerns stem from two key areas:
- Fabricated Information: Just like ChatGPT and its potential to generate fabricated legal cases, users worry that the AI tool may create non-existent information within the summarized points or answers provided to their queries.
- Incomplete Results: There needs to be more transparency in what the AI tool considers when returning search results. If the platform only shows the documents directly referenced in its answer without revealing the broader search scope, users may be unsure if relevant information was overlooked.
- Bias and Explainability: Can we ensure these models are unbiased and their decision-making processes transparent, allowing for human oversight and accountability?
- Authenticity and Trust: In an age of deep fakes, how can we guarantee the authenticity of AI-generated content and prevent its misuse as fabricated evidence?
- Security and Ethics: What are the implications of potential misuse of this powerful technology, and how can we establish safeguards to ensure ethical and responsible application?
Learning from the Past, Shaping the Future
By drawing parallels to predictive coding’s evolution, the eDiscovery ecosystem can approach generative AI with informed caution and harness its potential responsibly:
- Human Expertise Remains Indispensable: While AI automates tasks, human judgment remains crucial for interpreting results, managing ethical considerations, and ensuring legal compliance.
- Transparency and Explainability at the Forefront: Develop tools and frameworks that demystify generative models’ outputs, ensuring human oversight and accountability throughout the process.
- Bias Mitigation as a Priority: Employ robust data sets and validation processes to prevent biased outputs that could distort evidence or unfairly disadvantage parties.
- Collaboration for Shared Understanding: Industry stakeholders, including technologists, legal professionals, and regulators, should collaborate to establish ethical and responsible use guidelines for generative AI in eDiscovery.
- Continuous Learning is Key: As generative AI evolves, the eDiscovery ecosystem must adapt, fostering ongoing education and training for professionals to ensure responsible and effective use.
The impact of generative AI on eDiscovery is still unfolding, but the potential benefits are undeniable. By proactively addressing potential challenges and learning from the evolution of predictive coding, the ecosystem can harness the technology’s power while safeguarding transparency, fairness, and ethical principles. The journey might be complex, but by working together, the eDiscovery ecosystem can pave the way for a future where AI enhances legal processes without compromising integrity and justice.
Jon Fowler is an eDiscovery and digital forensic expert. He has a particular focus on financial regulatory investigations and large-scale complex litigation.