About
A model for human governance of AI
Loop names three things often collapsed into one phrase: reviewing outputs, monitoring systems, and owning outcomes. Human-Accountable-for-the-Loop — HAL — is the third pattern, and the one that needs a deeper accountability framework.
Why Loop exists
"Human-in-the-Loop" is one of the most repeated phrases in AI governance. The problem is that people use it to mean very different things: reviewing outputs, monitoring systems, or remaining accountable for outcomes. Those are not the same governance model.
Loop gives clearer language. It defines three patterns: review (Human-in-the-Loop), monitor (Human-on-the-Loop), and own (Human-Accountable-for-the-Loop). Each fits a different class of workflow.
Where HAL fits
HAL does not replace Human-in-the-Loop. It is the detailed accountability framework for workflows where individual review does not scale: agentic systems, multi-agent orchestration, large-scale triage, and autonomous operational systems.
HAL complements the standards organisations already follow. NIST helps you understand AI risk. ISO 42001 helps you build an AI management system. The EU AI Act defines regulatory obligations. Loop helps you identify which governance pattern applies. HAL answers whether a workflow is ready to delegate action while retaining accountability.
What we believe
- Precision matters. Calling something "Human-in-the-Loop" when no human can meaningfully review every output is a governance fiction, not a control.
- Accountability cannot be delegated. Execution can.
- The right governance pattern follows the workflow. It is not a maturity level, a compliance badge, or a ranking from weak to strong.
- Human oversight is not one thing. Reviewing, monitoring, and owning a system are different acts with different controls and different accountability structures.
Author
Loop and HAL were developed by Ryan McDonough, working at the intersection of legal technology and AI governance. The frameworks grew from a straightforward observation: the phrase "Human-in-the-Loop" was being used to cover reviewing outputs, monitoring systems, and owning outcomes — three very different governance positions — and that imprecision was creating real accountability risk as AI systems moved from producing drafts to taking actions.
Read the paper, explore the Loop model, and use the HAL framework, assessment, and calculator to put it into practice.
Execution can be delegated. Accountability cannot.