In the high-stakes world of complex litigation, a successful settlement is only the beginning. The true measure of justice often lies in the administration of the funds, a process increasingly strained by the collision of antiquated methods and modern digital realities. To explore this critical intersection, we are joined by Desiree Sainthrope, a legal expert whose work in trade agreements and global compliance has given her a unique perspective on the evolving role of technology and data science in law. Drawing on lessons from high-profile cases, she illuminates how a data-aware approach is no longer an advantage but a fundamental fiduciary duty.
This conversation delves into the systemic failures of traditional, document-heavy claims processing and the human cost of “documentation friction.” We explore how data-driven identity resolution offers a more accurate and equitable path forward, transforming fragmented digital records into verifiable proof. Sainthrope also provides a strategic roadmap for attorneys navigating e-discovery, explaining how to secure the right data to counter defense objections. Finally, we examine the ethical imperative for lead counsel to adopt these modern methods, the critical difference between “White Box” and “Black Box” systems, and what the future holds for data science in the legal field.
In the East Palestine derailment case, the initial administrator was removed after miscalculating distances and improperly denying claims. From your perspective, what specific failures in traditional, document-heavy processes lead to such systematic errors, and how can they be avoided from the start?
The breakdown in the East Palestine case is a painful but perfect illustration of why these legacy systems are failing. When you rely on a process that is fundamentally clerical and document-heavy, you are building a system on a foundation of potential human error. Imagine thousands of claims, each with a stack of scanned utility bills or statements. Someone has to manually verify addresses, check dates, and then plot those locations against a complex, multi-tiered map of impact zones. The initial administrator’s miscalculation of distances wasn’t just a single mistake; it was a systemic failure born from a process that is simply not built for modern scale or accuracy. These errors are almost inevitable when you’re asking people to perform the work of algorithms. The only way to avoid this from the start is to design the administration process around data science. You begin with the data as the source of truth, not a piece of paper that needs to be manually interpreted and entered into a spreadsheet.
Requiring displaced residents to produce years-old utility bills is often defended as a fraud prevention measure. Can you describe the real-world impact of this “documentation friction” on claim take-rates and explain how modern data validation offers a more accurate, less burdensome alternative?
This defense of “fraud prevention” is a fallacy that does immense harm to the very people a settlement is supposed to help. Think about the reality for a displaced resident. They may have lost documents in the chaos of an evacuation, or they may have switched to paperless billing years ago and no longer have access to old accounts. Asking them to hunt down a specific utility bill from February 2023 is not a simple request; it’s a significant hurdle. We call this “documentation friction,” and every piece of paper you demand acts like a tax on a claimant’s time and energy, causing many legitimate victims to simply give up. This friction systematically suppresses take-rates. The alternative, modern data validation, is not only less burdensome but far more accurate. Instead of a faded bill, we can use historical data records to verify residency with near certainty. This backend process finds the truth in the data itself, confirming a person lived at a specific address during the disaster without them having to lift a finger, which ensures valid class members are served, not obstructed.
For an attorney new to this, could you walk through the process of “identity resolution”? What key data points—like device IDs or timestamps—are used to connect fragmented digital records to a real person, proving their presence in a specific location without physical documents?
Absolutely. Identity resolution is the scientific process of connecting the scattered breadcrumbs of a person’s digital life back to a single, verified individual. A defendant’s database might have an email address associated with one activity, a phone number with another, and a physical address with a third, but no single record that contains all three. Identity resolution bridges those gaps. We start by gathering as many identifiers as possible—names, emails, phones, and addresses. But the real power comes from the less obvious data points: a unique device ID from a smartphone, an IP address with a timestamp that geolocates a user, or a defendant’s own unique identifier (UID) assigned to a customer account. By triangulating these fragmented pieces of information, often enriched with third-party data, we can create a complete and accurate profile of a class member. This allows us to prove, for instance, that a specific person’s device was consistently located within a high-risk zone during the relevant time period, providing concrete evidence without ever needing a utility bill.
During discovery, a defendant might claim it’s impossible to match fragmented digital records to actual people. How should a plaintiff’s attorney navigate this? What specific, non-obvious identifiers should they request to ensure they get the data needed for their own experts to analyze?
This is a classic move in the e-discovery chess match, and it’s one a data-savvy attorney must be prepared for. The defense will often hand over fragmented data and claim it’s impossible to connect it to actual people, essentially shrugging their shoulders. The key is to never rely on the defense to do the work. Instead, you must request the specific raw ingredients your own experts need to perform the identity resolution themselves. Go beyond the obvious requests for names and emails. You need to ask for the non-obvious connectors: the device IDs, the timestamped IP addresses, and any unique internal identifiers or UIDs they assign to users. When you get this fragmented data, you can then use your own experts to fill in the gaps and build a comprehensive class list. By demanding these specific data points, you sidestep the defendant’s argument entirely and empower your own team to uncover the full story hidden in the data.
Settlement funds can be diluted by 35-80% when antiquated processes fail to stop sophisticated fraud. How does relying on these older methods create a potential breach of fiduciary duty for lead counsel, and what does a proper technical audit of a claims administrator look like?
Relying on these antiquated methods is becoming increasingly indefensible and walks right up to the line of a breach of fiduciary duty. Lead counsel has a core obligation to protect the class and maximize the recovery for every legitimate claimant. When you choose an administrator who uses a simple, document-based process, you are essentially leaving the door wide open for sophisticated fraudsters who can easily generate synthetic identities and fake documents. Seeing a fund diluted by anywhere from 35 to 80 percent due to fraud is a catastrophic failure that directly harms the real victims. A proper technical audit involves going under the hood of your administrator’s process. You have to ask them to demonstrate their logic for fraud detection. Are they just sending out expensive cure notices, or are they using advanced data science to spot anomalies and suspicious patterns? You need to ensure their filters are precise enough to stop bad actors without improperly blocking legitimate class members. It’s no longer enough to just trust; you have a duty to verify their technical competence.
You differentiate between “White Box” and “Black Box” systems for evaluating claims. Could you explain this distinction and provide an example of a question a litigator must ask a potential partner to ensure their claim evaluation logic is defensible before a judge?
This distinction is absolutely critical. A “Black Box” system is one where the administrator can’t fully explain the logic behind their decisions. They might say a claim received a “low confidence score” and was denied, but they can’t articulate the specific data points or rules that led to that conclusion. It’s an opaque, proprietary algorithm, and that is completely indefensible in court. A “White Box” system, on the other hand, is entirely transparent. Every step of the evaluation logic is documented and can be clearly explained. If a claim is flagged, you can point to the exact reason why. A litigator must ask a potential partner a direct question like: “If a claim is denied, can you provide me with a clear, step-by-step audit trail that explains the precise logic and data points used to arrive at that determination, in a way that I can present and defend before a judge?” If they hesitate or talk about proprietary methods, that’s a massive red flag. You cannot afford to stand before a judge and say you don’t know how a claim was adjudicated.
What is your forecast for the legal industry’s adoption of data science in class action administration over the next five years?
Over the next five years, I predict we will see a dramatic and court-mandated shift from this being a niche expertise to a baseline requirement for competency in class action litigation. Cases like the one in East Palestine are serving as a harsh wake-up call, and judges are becoming far less tolerant of administrative failures that harm class members. We will see the standard of care for fiduciary duty evolve to explicitly include technical and data-related oversight. Law firms that fail to develop this internal “data facility” or partner with true data science experts will not only achieve poorer results for their clients but will also face a growing risk of malpractice claims and court sanctions. The era of simply outsourcing administration to the lowest bidder without rigorous technical diligence is over. Data science will become as fundamental to the practice of complex litigation as legal research and writing.
