Ethotechnics Institute · Open commons

Make high-stakes AI easier to stop, explain, appeal, and repair

When an organization lets software decide who gets a loan, a shift, a benefit, or a refund, it hands over part of its own authority. This site is about keeping that handover reviewable: who authorized this kind of decision, on what evidence, until when, who is allowed to object, and what actually changes when they do.

Start with a live decision, an incident, or a policy gap. Leave with named owners, clocks, evidence, and next actions, using open standards, mechanisms, and diagnostics you can cite.

Other frameworks ask whether you managed risk responsibly. This one asks whether the system can still be stopped while it is harming someone, and whether the people carrying that harm can make it stop. See how it differs → How the method works →

For commercial application of the framework (clinical AI safety evaluation, VC diligence, and governance advisory), see Ethotechnics Studio.

Shared language

Plain definitions and reference data

Definitions for concepts like burden, contestability, repair, and stoppability, with stable links and JSON for audits, tools, and research. Use it when a team needs the same words to mean the same things.

Browse definitions → Use the JSON API →

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.

Use these when a real person, queue, or decision needs attention—not just documentation.

Audit and compliance

Map practical controls to standards and audits

Standards are written so teams can cite requirements, assign owners, and show evidence without translating abstract principles from scratch.

Regulatory mapping

Audit handoff

Adoption paths

How it's different

Existing standards ask: 'Did you manage risk?' Ethotechnics asks: 'Can this system be stopped when it's harming someone?'

Not a replacement for NIST, OECD, or the EU AI Act. The enforcement layer they leave undertreated.

Most AI governance frameworks improve documentation, oversight, and accountability. Ethotechnics addresses what they leave undertreated: whether a running system can actually be halted, reversed, and repaired under stress — on a clock that matters to the person being harmed.

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 with measurable performance properties — not a compliance posture. See the full standards comparison for the detailed analysis.

Use and cite this work

Free to use with attribution

Ethotechnics Institute materials are published under CC BY 4.0 .

Individual entries and patterns include citation metadata so you can reference exactly what you used.

New and updated

Recent releases

  • Evidence Pack Readiness diagnostic — v1.2 (March 2026)
  • Glossary: 8 new entries on decision latency and repair authority (February 2026)
  • Field note: Mapping invisible maintenance work in clinical AI (January 2026)

Studio

Need hands-on help?

Ethotechnics Studio provides commissioned support for clinical AI safety evaluation, diligence, governance design, and implementation work.

Ethotechnics Studio →