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  • Professionals (25)
  • Capabilities (11)
  • Experience (8)
  • Insights & News (147)
  • Other Results (10)

Professionals 25 results

Dan Webb
Dan Webb
Partner
  • Chicago
Email
+1 312-558-5856
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Partner

  • Chicago
Arthur B. Schoen
Arthur B. Schoen
Associate
  • New York
Email
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Associate

  • New York
Chaoxuan Charles Liu
Chaoxuan Charles Liu
Of Counsel
  • Dallas
Email
+1 214-453-6423
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Of Counsel

  • Dallas
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Capabilities 11 results

Practice Area

Product Liability & Mass Torts

Major multinational companies trust Winston to defend their products and reputations in high-profile, high-stakes product liability and mass tort claims. Our Product Liability & Mass Torts Practice is one of the industry’s most seasoned, with a deep bench of experienced lawyers and a unique combination of extensive trial experience and expansive technical knowledge....Read more

Practice Area

Crisis Management

Addressing a crisis in today’s climate requires both swift action and careful consideration of all potential risks. Winston & Strawn is a trusted business adviser to clients facing complex, fast-moving, high-profile crises....Read more

Practice Area

Environmental Litigation & Enforcement

With today’s rapidly evolving rules of engagement, paired with the often contentious nature of environmental disputes, clients rely on Winston for our practical experience and substantive knowledge of the broad-ranging issues involved in environmental litigation, enforcement, and investigations....Read more

Experience 8 results

Experience

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July 8, 2024

Winston assists Milexia in Acquiring Cabcongroup

Experience

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January 16, 2024

Represented The Honey Pot Company Holdings, LLC, a leading better-for-you feminine care brand, in the sale of a controlling stake in the business to Compass Diversified (NYSE: CODI)

Experience

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November 13, 2023

Winston Represented Precision Aviation Group in $400M Single-Asset Continuation Fund Raise with GenNx360 Capital Partners

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Insights & News 147 results

Product Liability & Mass Torts Digest

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May 8, 2025

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4 Min Read

EDNY Allows Expert’s Use of ChatGPT to Confirm Alternative Design in Product Liability Lawsuit

The U.S. District Court for the Eastern District of New York recently allowed an expert’s use of ChatGPT to confirm a proposed alternative design in a product liability lawsuit, denying the defendant’s motion to exclude the expert testimony for failing the reliability requirement of Federal Rule of Evidence (FRE) 702.

Product Liability & Mass Torts Digest

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April 3, 2025

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5 Min Read

Supreme Court Has a Chance to (Re-)Clarify Albrecht Impossibility Preemption Test

A cert petition filed in March in the long-running In re Fosamax (Alendronate Sodium) Products Liability Litigation gives the Supreme Court a chance to clarify—for the second time—the Third Circuit’s restrictive application of the impossibility preemption defense.

Product Liability & Mass Torts Digest

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March 27, 2025

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3 Min Read

A Natural Solution: Court Dumps Microplastic Suit for Lack of Scientific Evidence

The U.S. District Court for the Northern District of Illinois recently dismissed a putative class action complaint alleging the term “natural” on Fiji Water bottle labels is false and misleading due to alleged microplastics contained in the water, because the plaintiffs failed to provide specific evidence to plausibly establish such contamination.

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Other Results 10 results

Site Content

What Is General Causation?

In the product liability context, general causation is the legal and scientific determination of whether the product in question has the inherent capability to cause the alleged injury under certain circumstances. Establishment of general causation is a critical aspect of a plaintiff’s case, and typically requires scientific evidence, such as epidemiology or toxicology, and expert testimony to establish a causal connection between the product’s design, manufacturing, or warnings and the injury or injuries at issue. As contrasted to specific causation, the focus in a general causation inquiry is on whether the product or exposure, as a general matter, can cause the type of injury claimed by the plaintiff. A general causation inquiry does not assess any facts unique to a particular plaintiff....Read more

Site Content

What Is Specific Causation?

Specific causation is the legal and scientific determination of whether the product in question was the direct cause of a plaintiff's specific injury. While general causation inquires whether a broader connection exists between a product and a type of harm, a specific causation assessment focuses on the unique circumstances surrounding a particular plaintiff's case. It typically requires expert testimony demonstrating that the alleged defect or issue in the product was responsible for the specific injuries suffered by the plaintiff. Alternative explanations for the plaintiff’s specific injuries must also be ruled out—typically via a differential etiology—for specific causation to be established....Read more

Site Content

What Is Generative AI?

Generative AI tools can create new content, such as text, computer code, images, audio, sound, and video, in response to a user’s prompt, often in the form of a short written description of the desired output. Generative AI tools are based on machine learning, trained using enormous amounts of data.[1] Generative AI tools are built on a system of inputs and outputs. First, the tool goes through a machine learning period whereby it is trained to generate predictive models and creative outputs through a large data set, often varied and diverse but tailored to the goal of the tool (i.e., customer service, generating scientific or marketing models, etc.). For in-house tools, this can be done with the company’s own data; for larger tools such as ChatGPT, this is done with the creator’s data set.[2] Once the tool has been trained, the individual user “inputs” a short prompt for the tool to synthesize and produce an “output.” Inputs are often retained on the servers controlled by the company that supports the tool, for monitoring of the tool’s performance and, in some cases, continued learning. The “outputs” are created by combining the machine learning during the training period with the inputs to produce an output.[3]...Read more
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