.png){kind=icon}
January 2, 2024
Masimo v. Apple - How Generative AI Platforms Transform Patent Litigation
Proactive vs. Reactive litigation practices
Read More
.png){kind=small}
Joshua Masia
December 15, 2023
In the ever-evolving landscape of litigation finance, my recent panel participation served as a catalyst for a deeper exploration of the transformative power of artificial intelligence (AI). This reflection piece combines insights from a unique perspective on Generative AI with the broader industry's focus on machine learning (ML) methodologies. It aims to provide a holistic overview, encompassing the nuances of private data utilization and the importance of a walk-to-run approach.
The legal tech landscape spans a spectrum of innovations, from virtual hearings to advanced data analytics and Artificial Intelligence. In our focus on harnessing Generative AI to redefine how litigation finance deals are originated and distributed, we recognize that the heart of legal tech lies in effective data management, setting the stage for a dynamic shift in the traditional human-machine interaction.
Litigation finance has traditionally been slow to adopt technological advancements. However, a technological breakthrough addresses industry needs for efficient deal origination and due diligence, automating information extraction and normalization. The goal is to eliminate manual processes, leaving professionals with more time for meaningful relationship building.
While AI, including Generative AI, is not a direct analytics tool, it plays a vital role in organizing vast datasets. Ensuring that data is not only organized but is also ready for seamless analytics allows for more informed decision-making during the litigation funding process.
The panel discussion highlighted the question of whether AI should replace or complement human decision-making. The stance is clear: AI is a tool that enhances human capabilities but does not replace them. From case origination to underwriting, Generative AI keeps the human in the loop, offering a synergy that leads to more effective outcomes.
Traditional deal management tools have often fallen short in providing the flexibility and collaboration capabilities necessary for the unique nature of litigation finance. Introducing the concept of Deal Relationship Management, allowing for audibles, stakeholder collaboration, and streamlined processes. The platform ensures deals move swiftly towards closure.
Many panelists emphasized using ML, particularly for forecasting case outcomes. However, there's a potential missed by focusing solely on public data. This reflection piece introduces the walk-to-run approach, showcasing the differences between Generative AI and ML. Generative AI allows organizations to utilize private data for training, unlocking unseen potential and offering a tailored, organization-specific approach.
Causality is a critical factor when dealing with legal matters. Generative AI, with access to private data, allows for a more granular understanding of causality. Unlike generalized algorithms, it has the potential to unravel the intricacies of individual cases, providing a deeper level of insight into the factors influencing case outcomes.
To understand the trade-offs between Generative AI and ML, a glimpse into the mathematics is essential. AI, with access to private data, can be more robust in capturing complex relationships, leading to better predictive accuracy. This is particularly evident when dealing with unique case scenarios that might not be adequately represented in public datasets.
It's crucial to recognize that the value of direct access to private case data can make a huge difference in automating selection. When considering the confidence of the models, even achieving a 1% increase in accuracy is a massive lift. Each percentage point is sometimes exponentially hard to reach, especially as we aim for an unachievable 100%. Private data sets can play a pivotal role in bridging this gap, making a significant difference as we approach the limits of predictive accuracy.
Adopting a walk-to-run approach requires a shift in mindset. Industry players need to recognize the value of managing private datasets effectively. While ML provides quick utilities for case feedback, starting with Generative AI and managing your own datasets offers a more comprehensive, organization-specific approach. Education is key to overcoming cultural barriers and ensuring a smoother transition.
The walk-to-run approach emphasizes the importance of evolving from basic utilities to sophisticated, organization-specific models. While ML serves its purpose, Generative AI unlocks untapped potential, providing a deeper understanding of the intricacies of each unique case. As we move forward, a balanced integration of both approaches can offer a holistic perspective on the role of AI in litigation finance.
To watch the panel discussion in full: