Defending the Algorithm®: A Lawyer's Bayesian Analysis of AI Litigation and Law

Epizódok

• 
  When the Chatbot Reopens a Closed File: Nippon Life v. OpenAI and the End of “Final” 26.08.2026 31p

  In Nippon Life Insurance Company of America v. OpenAI Foundation and OpenAI Group PBC, No. 1:26-cv-02448 (N.D. Ill.), a life and disability carrier is suing the maker of ChatGPT because — Nippon alleges — the chatbot talked a claimant into blowing up a settlement she had already signed, then drafted the flood of filings she used to do it. It may be the first major civil case to accuse a consumer AI product of practicing law without a license. For the insurance bar, it is something more immediate: a live demonstration of what happens when the person on the other side of your closed claim has a tireless, confident, always-available “lawyer” who never went to law school and never passed a character-and-fitness review. Every insurance claims professional in the country has been trained to believe in one word: final. A release is signed. A dismissal with prejudice is entered. The file is closed, the reserves come down, and everyone moves on. No more legal spend. That word — final — is the load-bearing beam of the entire insurance claim and lawsuit settlement economy. This is Edition 2 of our Defendng the Algorithm™: AI and Insurance Law Insights series.
• 
  Filling the AI Liability Gap: Should Asimov’s Three Laws Be Codified to Permit a Tort Cause of Action for Foreseeable Harm? 16.01.2026 44p

  When Isaac Asimov, a biochemistry professor turned science-fiction author, began publishing stories about “robots” in the 1940s, he was—though he did not call it that—writing about artificial intelligence. His creations were not lumbering automatons of steel and wire but synthetic minds capable of reasoning, learning, and moral judgment. In the I, Robot stories and subsequent novels, Asimov’s robots processed information, interpreted human language, and confronted the ethical consequences of their decisions. In that sense, his “robots” were conceptually indistinguishable from what we now call AI systems. The irony, of course, is that today’s so-called “artificial intelligence” is still far less sophisticated than Asimov’s imagined machines with their “positronic brains.” Our chatbots and generative models can emulate language and pattern recognition but lack true self-awareness or moral reasoning. They are, if anything, the rudimentary predecessors of Asimov’s robots—embryonic steps toward the cognitive and ethical autonomy he foresaw. Yet it is precisely because we have not yet reached his vision that the legal questions he implied have become urgent. Asimov assumed his robots would be bound by moral imperatives—the “Three Laws of Robotics”—to prevent harm, obey human commands, and preserve themselves. Modern AI systems, by contrast, operate within no comparable framework of codified ethical restraint.
• 
  A 2025 AI and Trade Secret Law Retrospective: What This Year’s Cases Teach Us About Protecting AI Systems 31.12.2025 20p

  Blog and Podcast #7 in the Series: Defending the Algorithm™: A Bayesian Analysis of AI Litigation and Law. As 2025 draws to a close, the trade secret landscape has shifted in ways that matter profoundly for companies developing and deploying artificial intelligence. Throughout this Defending the Algorithm™ series, we have argued that trade secret protection operates not as a binary guarantee but as a Bayesian probability—a likelihood we must continuously update as courts issue rulings and technology evolves.
• 
  The Lokken v. United Health Care Discovery Battle and What It Means for Insurers Using AI 16.12.2025 41p

  Podcast #6 in the Series: Defending the Algorithm™: A Bayesian Analysis of AI Litigation and Law
• 
  When the Algorithm Speaks for Itself: Raine v. OpenAI and the Future of Section 230 Immunity 07.11.2025 12p

  In August 2025, a pair of California parents filed suit in California state court against OpenAI (Raine v. OpenAI, Inc.) after the death of their teenage son, alleging that the company’s generative-language model played a direct role in his suicide.[1] According to the Raine v. OpenAI Complaint, the boy had used ChatGPT thousands of times over the course of a year, shifting from homework assistance to increasingly personal conversations. As his mental state deteriorated, the chatbot allegedly “became his closest confidant,” at times “offering methods of self-harm” rather than deflecting or referring him to help.[2] The family asserts theories of product design defect, negligent failure to warn, and wrongful death—claims that place the system itself, not any human user, at the center of the causal chain.[1] Raine v. OpenAI, Inc., Case No. CGC-25-628528 (S.F. Cnty. Super. Ct. filed Aug. 26, 2025); see also Nate Raymond, “OpenAI, Altman Sued over ChatGPT’s Role in California Teen’s Suicide,” Reuters (Aug. 26, 2025), https://www.reuters.com/sustainability/boards-policy-regulation/openai-altman-sued-over-chatgpts-role-california-teens-suicide-2025-08-26.[2] First Amended Complaint, Raine v. OpenAI, Inc., ¶¶ 2, 33 (S.F. Cnty. Super. Ct. Oct. 22, 2025), available at https://assets.alm.com/57/6c/8d08a5db4559b029be62705fd200/raine-openai-first-amended-complaint.pdf
• 
  The Daily AI Routine: A Practicum for Using AI Tools in Context - in Business and IP Litigation 28.10.2025 49p

  In this Defending the Algorithm™ blog and audioseries, we've examined the important Bartz v. Anthropic copyright settlement, explored the expanding legal battleground over AI training data beyond copyright claims, and calculated trade secret litigation probabilities. Throughout these analyses, I've disclosed that I use AI tools extensively in my legal practice—from researching and writing pleadings, briefs and strategy memos, to writing these posts and creating marketing materials. Obviously, we all use AI to manage our daily litigation workload, to sort and filter emails,to store documents to files, and to manage and generate large document reviews, analyses, coding and the production of documents. Consider this a practicum—a practical guide demonstrating that thoughtful AI implementation—involvingproper information management, Bayesian thinking and rigorous user training—can enhance rather than replace or compromise the quality of legal services.This blog post is another in the series “Defending the Algorithm™” edited by Henry M. Sneath, and was authored completely by Claude® from Anthropic Sonnet Edition 4 with editing assistance from the Human Editor. Claude can make mistakes so please double-check this content if you intend to rely on it. 
• 
  Trade Secrets and AI Collide in OpenEvidence v. Pathway Medical Inc. 25.10.2025 26p

  This Post was Guest Authored, edited and narrated by Acacia Perko, a business, IP and Trade Secret attorney with Houston Harbaugh in Pittsburgh. Artificial intelligence is reshaping industries—from drug discovery to finance to e-commerce—but it is also reshaping the legal strategies businesses use to protect their innovations. Traditionally, intellectual property (IP) protection has revolved around copyrights and patents. Now, however, trade secrets are emerging as a powerful (and sometimes safer) alternative.AI innovations are often “black boxes,” making them difficult to patent and hard to regulate. That uncertainty makes trade secrets not just an alternative, but in many cases the most practical protection available. The challenge for businesses is balancing the power of AI with the discipline of secrecy.Handled wisely, trade secrets can give companies a competitive edge in an era where information is both the most valuable resource—and the easiest to lose.For more information please contact the author Acacia B. Perko at 412-288-4016 or [email protected].
• 
  Copyright and Beyond – The Expanding Legal Battleground Over AI Training Data and AI Enterprise Software 25.10.2025 37p

  Our previous analysis of the historic $1.5 billion Anthropic settlement in Bartz v. Anthropic revealed how Judge Alsup's groundbreaking ruling established a potential bright line legal framework distinguishing between permissible AI training on legally obtained copyrighted works, and impermissible use of pirated materials. While this decision provides initial crucial guidance to legal practitioners on copyright fair use in AI training, it represents only the opening chapter in a rapidly expanding legal battleground over how AI companies acquire and use their training data, and how companies deploy AI enterprise software in the delivery of products and services. This blog post is another in the series "Defending the Algorithm™" written and edited by Pittsburgh, Pennsylvania Business, IP and AI Trial Lawyer Henry M. Sneath, Esq. and was authored with research assistance by Claude® from Anthropic Sonnet Edition 4.5 Pro and some research confirmation from Google Gemini AI 2.5 Flash. This series focuses on AI, the legal practice, and the intersections of AI and substantive law.
• 
  Defending the Algorithm™ #1: Historic $1.5 Billion Settlement: What the Bartz v. Anthropic Copyright Case Means for AI and Creative Industries 25.10.2025 14p

  The artificial intelligence landscape shifted dramatically this month when Anthropic, the company behind the popular Claude chatbot, agreed to pay $1.5 billion to settle a class-action copyright lawsuit brought by authors and publishers. This landmark settlement, which is awaiting court approval from Judge William Alsup in the Northern District of California at 24-cv-05417-WHA, represents the largest copyright recovery in U.S. history and establishes important precedentsfor how AI companies can legally use copyrighted material to train their systems. This blog post is another in the series “Defending the Algorithm™” edited by Henry M. Sneath, and was authored completely by Claude® from Anthropic Sonnet Edition 4 with editing assistance from the Human Editor. Claude can make mistakes so please double-check this content if you intend to rely on it.