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. The NIST AI Risk Management Framework (AI RMF 1.0) supports managing risks to people, organisations and society. ISO/IEC 42001:2023 specifies requirements for an AI management system. The EU AI Act creates legal obligations for actors and systems within its scope. Loop helps identify which governance pattern applies; HAL examines whether delegated action remains bounded, evidenced and accountable.
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.
- Human presence is not human judgement. An operating model that relies on capable reviewers, monitors or owners must also develop and maintain that capability.
Licence
The Loop model, the HAL framework, the eight domains, the assessment methodology, the worked examples, and the governance templates are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).
You are free to use, share, adapt, and build on the framework for any purpose, including commercially, as long as you provide attribution:
Loop / HAL Framework by Ryan McDonough, theloop.legal
The site code is separately licensed under the MIT Licence. Full licence terms are in the LICENSE.md file.
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. That imprecision was creating real accountability risk as AI systems moved from producing drafts to taking actions. Though they originated in legal technology, the governance problem they address (how to retain human accountability when AI systems act) applies equally across financial services, healthcare, HR and any sector deploying agentic workflows.
The full treatment of the framework is in the book Human Accountable for the Loop, available free or pay what you want on Leanpub. 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.