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Editorial Board

When LLM Optimization Becomes Liability

When LLM Optimization Becomes Liability

Why Enterprises Cannot Disclaim Consumer Harm Caused by LLM “Optimization” AIVO Journal — Governance Commentary Enterprises are increasingly using LLM optimization platforms to shape how their brands, products, and services appear inside AI-generated answers. These systems promise improved visibility across conversational interfaces that enterprises do not control. What is less

When AI Output Becomes Evidence: The Governance Gap No One Owns

When AI Output Becomes Evidence: The Governance Gap No One Owns

AIVO Journal — Governance Analysis The emerging failure mode Enterprise AI risk discussions remain dominated by questions of accuracy, bias, hallucination, and model safety. These concerns are legitimate, but they obscure a quieter and more immediate failure mode that is already materialising across industries: AI-generated claims are being relied upon

When Accurate Becomes Indefensible

When Accurate Becomes Indefensible

Decision-Shaped AI Reasoning as an Immediate Governance Exposure in Regulated Healthcare Contexts AIVO Journal - Anonymised Governance Case Study Executive Framing (for Risk and Finance Leadership) This case study examines a class of AI risk that is already operational, externally generated, and materially ungoverned: decision-shaped AI output produced under

Making AI Reasoning Inspectable Without Arguing Truth

Making AI Reasoning Inspectable Without Arguing Truth

AIVO Journal — Governance Commentary As AI systems become operationally influential, the central risk is no longer whether they can generate fluent answers. It is whether their reasoning can be inspected after the fact. Enterprises are now routinely affected by external AI systems they do not control: large language models mediating

Healthcare and Pharma: When AI Misstatements Become Clinical Risk

Healthcare and Pharma: When AI Misstatements Become Clinical Risk

AIVO Journal — Sector Governance Series AI assistants now influence how patients, clinicians, caregivers, and even regulators understand medicines, devices, indications, contraindications, risks, and clinical options. These systems answer questions long before individuals reach official channels. They often shape expectations about treatment pathways, benefit risk profiles, safety considerations, and comparative options.

Stable Visibility, Collapsing Selection: The Inertia Trap in AI-Mediated Decision Making

Stable Visibility, Collapsing Selection: The Inertia Trap in AI-Mediated Decision Making

Case Study Case Type: Anonymised Sector: Over-the-Counter Healthcare Risk Class: Externally Mediated Representation Risk Primary Failure Mode: PSOS–ASOS Divergence Status: Canonical Reference Case 1. Case Definition This case examines a category-defining, highly trusted, regulated consumer brand that maintains persistent visibility in AI assistant responses while systematically

The Next Phase of AI Will Not Be Smarter: It Will Be Accountable

The Next Phase of AI Will Not Be Smarter: It Will Be Accountable

The debate about large language models and world models has been useful, but it is already incomplete. Whether current systems lack grounded internal representations is no longer the most consequential question. The more important shift is this: AI systems are becoming operationally influential before they are epistemically reliable. That inversion