ĀML™ Explained: The Accountability Layer for AI-Generated Interfaces
ĀML™ makes the decision inspectable.
ĀML™ — ĀRU Meaning Language™ — is an experimental accountability layer for AI-generated interfaces. It gives software a structured way to carry meaning, policy, provenance and evidence alongside the decision about what a person sees.
↓
POLICY
↓
DECISION
↓
EVIDENCE
ĀML™ explains why.
ĀML™ does not need to replace HTML, React or the modern web. It can sit above or beside existing interface technology as a policy, provenance and evidence layer.
Describe structure, components, behavior and presentation. They answer: What should the browser render?
Carries declared purpose, policy inputs, provenance and evidence. It asks: Should this render under the declared policy?
One experimental policy in the current public prototype is deliberately simple:
// public experimental rule render_allowed = restoration_value >= attention_cost // example restoration_value = 1 attention_cost = 5 → SUPPRESS
Add accountability.
Existing interface elements can be connected to ĀML™ meaning and policy data. The browser continues rendering ordinary web technology while ĀML™ provides an inspectable policy checkpoint.
<button data-aml-id="optional-upsell" data-aml-purpose="promote_optional_upgrade" data-aml-attention="5" data-aml-restoration="1"> Upgrade now </button>
Decision → Receipt.
A decision does not have to disappear after the interface renders. ĀML™ research includes receipts, hashing, provenance and replayable evidence so important decisions can be inspected later.
Describe the purpose and policy-relevant inputs associated with the interface.
Run the declared inputs against an explicit deterministic rule.
Produce an inspectable machine-readable governance decision.
Bind the result to hashes, provenance and replayable records.
A meaningful test uses an actual screen, preserves the exact snapshot, records where the labels came from, executes the policy and publishes the outcome — including inconvenient outcomes.
Test the boundary.
Exact screen. Exact labels. Provenance. Deterministic decision. Reproducible evidence.
- A working open-source research prototype.
- A deterministic interface-policy system.
- A way to make declared meaning and evidence inspectable.
- A system that can bind decisions to receipts and hashes.
- An invitation for outside engineers to reproduce and challenge the work.
- A scientific measurement of human attention or wellbeing.
- Automatically correct when dishonest labels are supplied.
- A ratified global or government standard.
- Independent validation merely because internal CI passes.
- A universal detector of manipulation or human harm.
to generating human experiences.
When software dynamically decides what people see, organizations may need to answer harder questions: Why did this appear? Which policy allowed it? Who supplied the inputs? What evidence exists? Can somebody else reproduce the result?
Attach explicit meaning and policy to important interface decisions instead of burying intent inside prompts or application code.
Insert a governance checkpoint between generated interfaces and final rendering without replacing the entire front-end stack.
Inspect the inputs, rule, decision, provenance and evidence associated with a critical interface.
Try it. Break it. Reproduce it. Publish PASS, FAIL or MIXED evidence.
ĀML™ can govern and explain the decision to render it.
For organizations exploring AI interface accountability, ĀML™ research, commercial integration, licensing or strategic collaboration, contact ĀRU Intelligence™.
Company information, research, projects and current contact details are maintained on the official ĀRU Intelligence™ website.
Verify it first.
Clone the repository. Run the proof. Inspect the policy. Challenge the labels. Test a real screen. Publish the evidence.
Try it. Break it. Verify it. Publish the result.
ĀRU Intelligence™
AI Interface Firewall™
View Meaning™
© 2026 ĀRU Intelligence Inc. All rights reserved.
© 2026 Daniel Jacob Read. All rights reserved where applicable.
ĀML™, ĀRU Meaning Language™, ĀRU Intelligence™, AI Interface Firewall™, View Meaning™ and associated names, logos, product names, service identifiers, slogans, terminology, visual identities and source-identifying brand elements are claimed trademarks and/or service marks of their respective owner(s), including ĀRU Intelligence Inc. and/or Daniel Jacob Read, as applicable.
Open-source code rights do not automatically grant rights to official ĀRU or ĀML trademarks, service marks, logos, certification language, trust marks, endorsement claims or proprietary commercial branding. Technical compatibility or conformance does not imply official ĀRU Intelligence™ endorsement, certification, sponsorship or authorization.
ĀML™ is currently a working research prototype. Attention-cost, restoration-value and related values are declared or modeled policy inputs and are not represented as objective psychological, medical, neurological or scientific measurements. Nothing on this page claims that ĀML™ is a ratified international standard, government standard, independently certified technology, scientifically validated measure of human harm or proof of broad commercial adoption.
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