The human kept in the decision.
Infrastructure for conscious presence in a world that rewards noise.
AI Oversight Evidence · Caneni

Your AI policy says humans oversee decisions.
Can you prove a human decision-maker
was actually there?

Every AI decision leaves a trace. Some are evidence. Most are gaps.
There is a methodology that makes human judgment verifiable.

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The gap

What exists

Tools that document what the AI did — audit trails, model monitoring, risk dashboards. Billions invested.

What's missing

A verifiable record that a human decision-maker decided. Not the model. A person — identifiable, accountable, on the record.

The same gap appears across AI governance, professional liability, and algorithmic decision-making: no verifiable record that a human was here and decided.

The record is a timestamped, structured entry — who reviewed, what they considered, what they decided — anchored independently of the AI system, in a form a regulator, auditor, or court expects.

Precedent · Israel · Supreme Court · 22 March 2026 · Case AAM 63194-08-25

Israel's Supreme Court ruled that a municipality acted «recklessly» by relying on an AI-generated output without verification — in a case involving a child with special needs. 30,000 NIS in legal costs.
Amendment 13 to Israel's Privacy Protection Law holds boards personally accountable for data protection practices — including AI systems that process personal data — in force since August 2025.
A policy is not enough. Proof is required.

What Caneni is

The founding methodology for AI Oversight Evidence.

Caneni documents the human decision as a verifiable, independently-anchored, dated record — a record that survives the system it was made in. Not another audit trail. A verifiable record that the right human was there — and decided.

AI Oversight Evidence is the first application — the door, not the whole building. Digital Relic Net is the discipline. Conscious Digital Presence is the frame.

Four ways in

Regulatory context

Israel · Amendment 13 · in force Aug 2025

Boards of directors hold personal accountability for data protection practices, including AI systems that process personal data. Active obligation, not passive policy. The Privacy Protection Authority has published draft guidance on AI-related obligations (April 2025) and is moving to active enforcement in 2026.

EU AI Act · Article 14 · defines the requirement

Article 14 defines human oversight as a requirement for high-risk AI systems. The EU recently extended the high-risk compliance timeline — meaning the window to build this capability correctly and early is open now. Read how Caneni frames Article 14 human oversight evidence.

Google has notified users of an upcoming Terms of Service update effective July 30, 2026, covering background service activity and placing full accountability for AI-generated content on the organizations using it — not on Google. Regulatory and platform trends converge: accountability shifts to those who act on AI output.

Definitions

AI Oversight Evidence
AI Oversight Evidence is verifiable documentation that a human decision-maker — not the AI model — reviewed and decided in an AI-assisted workflow. It records who reviewed, what they saw, and what they decided, in the form a regulator, investor, or court expects.
Conscious Digital Presence
Conscious Digital Presence is the balance between human agency and automation in an expanding digital environment — the framework that keeps human judgment as the operating center of AI-assisted decisions.
Digital Relic Net
Digital Relic Net is the emerging discipline of preserving verifiable traces of human judgment across AI workflows — the infrastructure that makes AI Oversight Evidence possible at scale.
Verifiable human judgment
Verifiable human judgment is documented evidence that a human exercised meaningful judgment — not just formal approval — in an AI-assisted process, structured for regulatory, legal, and audit scrutiny.
Automation bias and the evidentiary gap
Automation bias — the systematic tendency to accept AI outputs without sufficient verification, documented in human factors research — means that formal human presence in a workflow does not guarantee meaningful engagement. What Norbert Wiener identified in 1950 as the core risk of automated systems remains the gap AI Oversight Evidence is designed to close.

Provenance

Foundations
Protocol registry with dated records from December 2025.
Published
Source work published 24 April 2026.
Operator
IRMI LTD · Arad, Israel.
Version
v2.1 · 27 June 2026.

Caneni does not call itself the standard. Caneni names the category first and offers the first methodology to work in it. A standard is made by adoption.

Ecosystem map
Caneni methodology ecosystem — documented protocol graph, December 2025 to June 2026

Caneni methodology ecosystem · December 2025 – June 2026

References

Legal and regulatory references: Israel Privacy Protection Law Amendment 13 (in force August 2025); EU AI Act Article 14; Israel Supreme Court ruling AAM 63194-08-25 (22 March 2026); Google Terms of Service update notice (effective July 30, 2026).
Scientific references: Parasuraman & Manzey, Complacency and Bias in Human Use of Automation, Human Factors 52(3), 2010; Simons & Chabris, Gorillas in our midst, Perception 28(9), 1999; Wiener, The Human Use of Human Beings, 1950.

One question worth asking

If you cover AI regulation, sit on a board, build with AI, or advise organizations that do — can you prove a human decision-maker made the call?

Talk to Apotrop

Or write: hello@caneni.net

About Caneni · Article 14 human oversight evidence · Media background · Protect for boards and compliance · Privacy

Full methodology: canon.caneni.net