Lozen Advisory launches AI responsibility reconstruction framework

Lozen Advisory has published a new framework for determining what an AI incident record can and cannot prove about responsibility. The method is aimed at boards, lawyers, insurers, and regulators as AI governance evidence rules remain unsettled.

Why it matters: - AI incidents can leave a paper trail, but that record may not prove who had authority, control, or responsibility. - Lozen Advisory’s framework is built to help boards, general counsel, enterprise risk leaders, and insurers assess liability exposure before and after an incident. - The publication arrives as federal standards for machine-generated evidence remain unsettled, including Proposed Federal Rule of Evidence 707, which was sent back for revision after public comment.

What happened: - Lozen Advisory published Evidence-Based Responsibility Reconstruction for AI-Mediated Conduct on July 30, 2026. - The framework defines a formal method for evaluating whether surviving evidence from an AI incident can connect specific actors to specific governance acts. - Founder Akilah E. Kamaria said AI accountability requires more than naming a responsible party in a policy. - The complete definition, category boundaries, terminology, and supporting analysis are published on the Lozen Advisory website as the official version of record.

The details: - The framework centers on five questions: what the AI system did, what authority it was granted, which human and institutional actors set the conditions for its conduct, what digital record remains, and where responsibility can still be established or divided. - Lozen Advisory says the protocol gives institutional stakeholders terminology to audit AI oversight records, establish LLM governance structures, and navigate regulatory scrutiny. - The publication introduces the Responsibility Reconstruction Protocol℠ and the Lozen Evidence-State Scale℠. - The Lozen Evidence-State Scale℠ classifies records as Reproducible, Traceable, Attributable only, or Unrecoverable.

Between the lines: - The framework reflects a shift from broad AI governance language toward evidence-based standards that can hold up in legal, board, and insurance settings. - The emphasis on what can no longer be recovered suggests some incidents may leave gaps too large for clean accountability claims. - Lozen Advisory is positioning terminology itself as part of the governance toolset, not just the policy outcome.

What's next: - Boards, counsel, risk teams, and insurers are likely to use the framework as AI systems take on more autonomous functions and incident reviews become more contested. - The company says the framework will support oversight before incidents and reconstruction after them. - Lozen Advisory continues to offer related frameworks, including Disclosure-Independent Governance, the Power User Trap℠, the Name Standard℠, the Responsibility Reconstruction Protocol℠, and the Lozen Evidence-State Scale℠.

The bottom line: - Lozen Advisory is trying to turn AI accountability from a policy claim into an evidence test.