The Impact of Artificial Intelligence Tools in Mass Tort Discovery and Case Valuation 

“Artificial Intelligence” (“A.I.”) is properly defined as being any technology that uses machine learning, natural language processing, or any other computational mechanism to simulate human intelligence, including document generation, evidence creation or analysis, and legal research, and/or the capability of computer systems or algorithms to imitate intelligent human behavior. The New York Supreme Court further finds that A.I. can be either generative or assistive in nature. The Court defines “Generative Artificial Intelligence” or “Generative A.I.” as artificial intelligence that is capable of generating new content (such as images or text) in response to a submitted prompt (such as a query) by learning from a large reference database of examples. A.I. assistive materials are any document or evidence prepared with the assistance of AI technologies, but not solely generated thereby. Matter of Weber, 2024 N.Y. Misc. LEXIS 8609 (New York Supreme Court, 2024) 

Mass tort litigation, cases where many plaintiffs sue one or more defendants for harm caused by the same product, drug, or disaster is often complex, slow, and extremely expensive. But today, the rise of artificial intelligence (AI) is changing how law firms handle these complicated cases. AI is speeding up discovery (the process of gathering evidence), making it easier to value cases accurately, and helping both plaintiffs and defendants manage massive amounts of data. 

Unlike a class action, where one case represents a whole group, mass torts allow each plaintiff’s story and injuries to be heard individually. In each, thousands of people may bring suit over dangerous drugs, defective medical devices, or chemical exposures, and each claim has unique underlying facts and tens of thousands of pages of medical records, corporate emails, scientific reports, and more. Traditional legal teams spend months or even years sorting, reading, and analyzing this information. 

How Artificial Intelligence (AI) Tools are Transforming Discovery 

In today’s litigation landscape, the sheer volume of electronically stored information (ESI)—including emails, documents, and medical records—has become overwhelming. Particularly in complex cases like mass torts, manual document review is often impractical due to time and cost constraints. For example, in a hypothetical mass tort involving a defective pharmaceutical drug, the parties faced the challenge of reviewing over three million documents as part of discovery. Reviewing such a massive dataset manually would take months and cost hundreds of thousands of dollars, all while risking human error and missed evidence. 

To address this challenge, the defense team proposed using predictive coding, an AI-powered document review technology. Predictive coding works by having human reviewers label a small portion of documents as relevant or irrelevant, allowing an AI algorithm to learn patterns and then rank the remaining documents accordingly. This prioritization enables attorneys to focus on the most important documents first, drastically reducing review time and cost. 

The legal foundation for using AI in discovery draws heavily on precedent cases such as Da Silva Moore v. Publicis Groupe (2012), where the court first approved predictive coding as a valid discovery method. Judges recognized that AI-assisted review could be more efficient and accurate than traditional manual methods. Another case like Dynamo Holdings Ltd. P’ship v. Comm’r of Internal Revenue, Nos. 2685-11, 8393-12 (T.C. Sept. 17, 2014), highlighted the proportionality and reasonableness of AI-driven review, especially with very large document sets. Cases like Global Aerospace, Inc. v. Landow Aviation, L.P., No. CL 61040 (Vir. Cir. Ct. Apr. 23, 2012) have shown courts becoming more open to approving predictive coding even when parties do not fully agree on the approach. 

In practice, the use of predictive coding in the hypothetical case led to impressive results: 97% of irrelevant documents were filtered out automatically, review time was cut by 70%, and cost savings exceeded $150,000 compared to traditional review methods. Importantly, the AI system identified key documents related to internal risk reports and side effects that had been missed in earlier manual searches. Despite initial objections from opposing counsel, the court approved the predictive coding protocol, citing relevant precedents and emphasizing the importance of proportionality under discovery rules. 

This case study illustrates how AI-powered document review is transforming the discovery process by making it faster, cheaper, and more accurate. While courts continue to expect transparency, human oversight, and well-defined protocols when using these technologies, legal acceptance is growing steadily. For law firms and legal teams facing high-volume discovery, AI tools like predictive coding are no longer just innovative options—they are becoming essential to efficiently meet discovery obligations in complex litigation. 

Recent Cases and Judicial Trends 

Courts are focusing sharply on technical transparency and defensible methodologies. In a California MDL proceeding in 2025, judges set precedent by demanding early electronically stored information (ESI) protocols and robust AI disclosure during e-discovery—demonstrating that technical competence and openness about digital methods are now a baseline expectation. Similarly, in Garcia v. Character Techs., Inc, No. 6:24-CV-01903 (M.D. Fla. filed Oct. 22, 2024) (Character Tech) a Florida federal case lays down allegations that an AI chatbot was defectively designed for minors and failed to warn users tested how product liability doctrines intersect with contemporary AI platforms. These cases show that judges increasingly balance innovation benefits with privacy concerns, and the intent to regulate AI-driven output logs or require parties to demonstrate their workflows during deposition is now visible in major MDLs. 

AI and Case Valuation 

Beyond just discovery, AI now aids in putting dollar values on mass tort cases, a process called “case valuation” in the form of: 

• Integrated Data Analysis: AI can combine information from litigation documents, regulatory filings, scientific studies, even social media and news reports to create a dynamic, real-time risk picture for ongoing claims.  

• Forecasting Outcomes: By analyzing prior verdicts and settlement amounts for similar cases, AI models can suggest likely values for individual plaintiffs or for groups—helping both sides negotiate settlements more effectively.  

• Spotting Litigation Trends: AI tools look at court analytics, regulatory timelines, and even track which lawyers are filing new cases and where, helping firms anticipate where new claims may surface and which legal theories are gaining traction.  

In a practical law firm setting, a personal injury case involving a car accident illustrates how AI can effectively assist with case valuation outside of court. A client suffered whiplash and a mild concussion, resulting in $18,000 in medical expenses. The law firm used the Practice AI demand valuation tool, which analyzes similar jury verdicts, local jurisdiction data, and injury specifics to generate a predicted settlement range. In this case, the AI estimated the claim to be worth between $45,000 and $70,000. 

Using the AI’s output, the firm crafted a demand letter and entered negotiations with the insurance company, which initially offered $28,000. By referencing comparative data supported by the AI’s analysis, the attorneys ultimately negotiated a settlement of $55,000—squarely within the predicted range. This example shows that while AI may not yet be widely accepted as evidence in court, it can be a powerful tool for case assessment and negotiation strategy. It helps legal teams set realistic expectations, support arguments with data, and advocate more effectively for their clients. 

Pitfalls and Ethical Concerns 

While AI is powerful, it’s not perfect. Legal teams must monitor for “algorithmic bias” (unintentional discrimination), errors from poor data, and ensure the privacy of sensitive plaintiff records. Courts expect attorneys to understand the technology they use, address these risks in discovery protocols, and always maintain attorney supervision over AI-driven decisions.  

This has been enlightened in the recent case U.S. v. Hayes,¹ wherein an attorney submitted a brief containing AI hallucinations, then proceeded to make matters worse by denying it.  Assistant Federal Defender Andrew Francisco filed a motion to unseal which twice cited and quoted from a non-existent case.  When the government’s opposition brief said the case could not be found, the lawyer claimed the error was inadvertent and that the quoted passage came from another case. This also proved to be untrue. At the hearing, in answer to the Magistrate Judge’s repeated questions, Francisco refused to concede the cited case was not real. The court found that Francisco had submitted a fictitious case which had all the markings of a hallucinated case created by generative AI tools.  When the lawyer also denied using AI to draft the motion, the court found his response was not credible.  In a 28-page order dated January 17, 2025, the Magistrate Judge found Francisco’s persistent misstatements to the court were not inadvertent but knowing and made in bad faith. “Despite being provided multiple opportunities to candidly acknowledge and correct his errors as required under his duty of candor to the court, Mr. Francisco unfortunately failed to do so.”  Id. at 1064.  It ordered him personally to pay sanctions of $1,500 and further ordered the clerk of the court to serve a copy of the order on the bars of California and the District of Columbia where he was admitted, and on all of the other district judges and magistrate judges in the district.  Id. at 1073. 

Looking ahead, AI’s greatest impact may be its ability to shift legal strategy from reactive (“What happened?”) to proactive (“What’s likely to happen next?”). For example, if a new study suggests a product may be safe for some users, AI can instantly re-sort thousands of active claims affected by that finding and help defense teams move to resolve them right away. AI also helps plaintiff firms detect legal violations faster—tools like Darrow scan thousands of public documents, social media posts, and scientific reports to “discover” emerging mass torts before they become national headlines.