Ethotechnics Institute · Open Commons
Make high-stakes AI easier to stop, explain, appeal, and repair
Standards, an ontology, and diagnostics for governing decisions that have been delegated to machines. Other frameworks check whether risk was managed on paper. These check whether a running system can be halted, challenged, and reversed under stress.
Built from public failures such as Robodebt, where 470,000 unlawful automated debts ran for three years because each appeal fixed one case and none halted the system. How the method works → Five public failures, scored →
The method, in six questions
Six questions to keep answering when software decides.
Software decides who gets a loan, a shift, a benefit, or a refund. A system becomes unsafe when these six drift apart — each card names the failure it catches.
- Capability What can the system do?
- Authority What is it allowed to do, for whom, and until when?
- Evidence What evidence justifies that permission?
- Dependency How hard is it to switch off or replace?
- Standing Who can challenge a decision, and with what force?
- Correction What can still be fixed, and how fast?
Each of these maps to one of the twelve laws. Read the method →
Incident triage
What is going wrong right now?
Choose the closest failure mode. Each path starts with what to do now, then names who should own follow-up and what evidence to collect.
- Decision appealed High · Owner in 24h
- Model made a harmful error Critical · Pause authority needed
- Appeals or reviews are stuck High · Escalate in 4h
- A user was harmed Critical · Human response now
- No owner is assigned High · Assign owner
- Decision cannot be clearly explained Medium · Review evidence
- System cannot be quickly stopped or rolled back Critical · Activate hard halt
These routes are for a live case: a person, a queue, or a decision that needs attention now.
The claim, in one figure
A delegation can double without anyone deciding that it should.
Both grants below grow by the same two percent a month. One is reviewed only when a single step is large enough to notice, so it is never reviewed. The other treats every widening as a fresh authorization, so it can be read, questioned, and reversed. That difference is what the standards on this site are for.
Demonstration
The same growth, decided and undecided
Two grants widen by 2% a month for 3 years. The left one is reviewed when a single step reaches 5%; the right one records every widening as an authorization.
The right-hand grant's state_history, first three entries
of 37
-
{ from: none, to: allowed, reason: issued }scope ×1.00 -
{ from: allowed, to: allowed, reason: expansion }+2%, scope ×1.02, capacity re-checked -
{ from: allowed, to: allowed, reason: expansion }+2%, scope ×1.04, capacity re-checked
A demonstration, not a measurement of any deployment. The steps and the
threshold are in src/utils/ratchet.ts; the transition reason
expansion and the states are the ones
authority-grant.schema.json allows, and a test holds them there.
MEC-19 expansion review reads state_history, and the left-hand
grant gives it nothing to read.
This is Law XI of twelve. Read the laws → See what STD-08 requires →
How it's different
Existing standards check that risk was managed. Ethotechnics checks that a system can be stopped while it is harming someone.
It does not replace NIST, OECD, or the EU AI Act. It specifies the operational controls they leave undertreated.
Most AI governance frameworks improve documentation, oversight, and accountability. Ethotechnics addresses what they leave undertreated: whether a running system can be halted, reversed, and repaired under stress — on a clock that matters to the person being harmed.
When a system simplifies its own operations, the work it leaves undone falls on someone — the nurse re-routing a scheduler's misfires, the claimant re-proving a denial. That labor is part of the system, so the person doing it holds standing to correct it (Law VII). The burden should run uphill.
| Existing standard says | Ethotechnics requires |
|---|---|
|
"Maintain human oversight" EU AI Act, Art. 14 |
Named human with stop authority, tested halt path, recovery clock |
|
"Manage risks across the AI lifecycle" NIST AI RMF |
Measurable time-in-harm bounds, exercised rollback and restoration paths |
|
"Conduct conformity assessment" ISO/IEC 42001 |
Evidence that the system can be stopped mid-incident, not just documented as compliant |
|
"Implement responsible AI principles" OECD AI Principles |
Binding escalation: owner + timer + action, or the system degrades |
Ethotechnics treats governability as an operational capability, measured by properties such as time to halt and time to reverse, rather than as a compliance posture. The full standards comparison covers each framework.
Use and cite this work
Free to use with attribution
Ethotechnics Institute materials are published under CC BY-SA 4.0 .
Individual entries and patterns include citation metadata so you can reference exactly what you used.
New and updated
Recent releases
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Glossary v1.13.0
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Rewrote category descriptions and scope notes to the current framing, replaced vague or promotional definitions in 89 entries and 109 short definitions, removed unsourced adoption and effect claims, and cut filler words and redundant sentences from 38 entries and 20 short definitions. No term was added, removed, or renamed.
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Evals v1.6.0
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Adds seven draft cases for typed decision models: content that claims an approval (AGT-013), the threshold as a policy record (DEL-010), sampling what ran without review (CTL-010), shaping and selecting hops (CHN-003, CHN-004), reasons that belong to the decision (EXP-011), and issue rate against answer capacity (STA-013). 15 eval suites, 155 test cases.
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Research v1.2.0
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Marked the three publications as planned studies. Removed sample sizes, findings, and timeline entries that no published data supported, and four bridge artifacts that were never published.
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