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When AI Policies Fail: The AI Sanctions in Johnson v. Dunn and What They Mean for the Profession
The Johnson v. Dunn case marks a turning point in judicial tolerance for AI citation errors. Despite clear firm policies and experienced counsel, the court imposed severe sanctions, signaling that only individual verification, not institutional...
Court’s Use of a Special Master to Assist EEOC in Obtaining Discovery from Defendant
A Kansas court appointed a Special Master in EEOC v. Genesh, Inc. to resolve electronic discovery disputes, signaling a firm stance on procedural fairness.
Adobe’s Legally Grounded AI Model Offers a Blueprint for Responsible Innovation
Adobe sets a new benchmark for legal AI development with Firefly, a model trained exclusively on licensed data. Its compliance-first strategy highlights a path forward in a contentious landscape.
Document Correlation Under Fed.R.Civ.P. 34(b)(2)(E)
The Sievert decision reignites a long-standing dispute over whether Rule 34(b)(2)(E) mandates document correlation for ESI. While the majority rule says yes, the article proposes a metadata-based alternative that balances efficiency and fairness.
Navigating AI’s Twin Perils: The Rise of the Risk-Mitigation Officer
Generative AI is reshaping trust and accountability in the digital landscape, leading to the emergence of the AI Risk-Mitigation Officer role. This strategic position blends technical, regulatory, and ethical expertise to proactively manage AI risks,...
GENIUS Act Signed Into Law: A Game Changer for Legal Discovery and Information Governance
President Trump’s signing of the GENIUS Act introduces sweeping regulatory changes for stablecoins, redefining how legal professionals approach discovery, compliance, and information governance in blockchain-based environments.
Don’t Rush Past Relevance: Assessing the Discoverability of AI Prompts and Outputs
As AI becomes a staple in workplace tools, courts and counsel must evaluate whether AI-generated prompts and outputs meet legal thresholds for relevance, proportionality, and privacy in discovery.
Panel of Experts for Everyone About Anything – Part Three: Demo of 4o as Panel Driver on New Jobs
In his conclusion to his three part series, award winning blogger, attorney and AI pioneer, Ralph Losey tests 4o as a panel driver by feeding it an article on new jobs created as a result...
Panel of Experts for Everyone About Anything – Part Two: Demonstration by analysis of an article predicting new jobs created by AI
In this article, Ralph Losey continues discussing the software, Panel of Experts for Everyone About Anything, and its demonstration while exploring potential job roles arising from AI, particularly the “Sin Eater” concept proposed by Professor...
Document Content vs. Metadata in eDiscovery AI: A Clarification of Scope, Access, and Accuracy
Clear separation between document content and metadata is essential for accurate AI-driven eDiscovery. Without proper handling, legal teams risk flawed timelines, missed privilege indicators, and incomplete review.
Avoiding the Third Rail of Legal AI: Don’t Let the Machine Think for You
As AI becomes more powerful, legal professionals face growing pressure to rely on it for core tasks. But true responsibility means using these tools wisely, without surrendering judgment, accountability, or the human craft of legal...
Relativity Scales Generative AI Availability Across Asia
Relativity has expanded its generative AI tools, aiR for Review and aiR for Privilege, to five additional countries in Asia. This move empowers legal and compliance teams with scalable, secure solutions for efficient document and...