I built a tool to prove a human reviewed an AI decision

Caneni, a new tool from an unnamed developer, creates verifiable records that a human reviewed an AI decision, addressing regulatory requirements such as Israel's Amendment 13 and the EU AI Act. The tool documents timestamped, structured entries of human oversight, aiming to provide proof for boards and compliance amid growing accountability for AI-generated content.

Every AI decision leaves a trace. Some are evidence . Most are gaps . There is a methodology that makes human judgment verifiable. Tools that document what the AI did — audit trails, model monitoring, risk dashboards. Billions invested. 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. 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. 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. 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 The Privacy Protection Authority has published draft guidance on AI-related obligations April 2025 and is moving to active enforcement in 2026. 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. 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. 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. 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. 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? Or write: hello@caneni.net mailto:hello@caneni.net About Caneni https://caneni.net/about/ · Media background https://caneni.net/media/ · Protect for boards and compliance https://caneni.net/protect/ · Privacy https://caneni.net/privacy/