Human performer
Perceives, adapts, hesitates, compensates, speaks up, stays silent, and makes tradeoffs under real operating conditions.
AdaptsWhite Paper
HAOP: A Safety Framework for the AI Era
Read the paper introducing HAOP, the three performers, distinct failure signatures, accountability-by-control, grounding, pause authority, and the True Function Test.
Framework
AI governance asks whether the model is accurate, secure, private, explainable, or compliant.
HAOP asks whether the work system remains grounded in operational reality before AI-shaped outputs become consequential.
Most AI governance asks whether the model is accurate, secure, private, explainable, or compliant. Those questions matter. They are not enough when AI begins to classify risk, prioritize signals, route work, recommend controls, approve action, or shape what a human reviewer sees first.
HAOP looks at the work system: human adaptation, AI optimization, and organizational signaling. It asks whether the system remains grounded in operational reality before AI-shaped outputs become consequential.
Built for EHS leaders, safety professionals, operational risk teams, AI governance leaders, and organizations deploying AI into consequential work.
The three performers
Different performers. Different failure signatures.One operating system.
HAOP treats the work system as a three-performer system so governance can follow how work is actually produced. Human performers adapt to real conditions. AI performers optimize against representations, signals, constraints, and permissions. Organizational performers create the container through objectives, incentives, resources, metrics, authority, and tolerated tradeoffs.
Recognizing all three performers creates a more holistic and practical, operational-level governance approach: it shows where control sits, where verification must occur, and where accountability has to be designed before the workflow becomes consequential.
AI performer
Classifies, predicts, generates, routes, recommends, prioritizes, escalates, suppresses, and optimizes based on signals and architecture.
OptimizesOrganizational performer
Authorizes, funds, measures, rewards, constrains, validates, ignores, normalizes, and assigns accountability.
AuthorsStart Here
The True Function Diagnostic is the entry point.
Most AI-enabled workflows can show that they manage safety. Fewer can show that they produce it.
Run the True Function Diagnostic to apply the True Function Test to one AI-enabled workflow and produce a True Function Alignment Map.
It is the beginning of HAOP implementation: a way to surface signal-objective mismatch, weak grounding, symbolic oversight, missing pause points, and accountability gaps before the workflow becomes consequential.
01Identify the true function
What outcome is the workflow supposed to produce in the real world?
02Test the representation
What does the AI see, miss, compress, distort, or prioritize?
03Map control
Who controls the signal, constraint, permission, metric, verification point, and pause path?
04Begin implementation
Use the findings to design verification, grounding, boundaries, and accountability into the workflow.
Roadmap
The first public tool is live.Additional HAOP tools are coming to continue the same logic of extending traditional HOP into framework exploration, accountability, oversight, workflow boundaries, and verification gates.
HAOP is being developed as a practical operating framework. The current public release includes the interactive white paper and the True Function Diagnostic. The diagnostic applies the True Function Test and produces a True Function Alignment Map. A framework exploration map is planned so visitors can move through the HAOP concepts without treating the homepage itself as the full exploration experience.
The goal is to help teams move from “we have AI governance” to a more concrete question: does this work system have the grounding, verification, constraints, control mapping, and pause authority needed to remain safe as AI changes the speed and shape of work?
Available now
True Function Diagnostic
Interactive tool that applies the True Function Test to one workflow and produces a True Function Alignment Map.
In development
Accountability-by-Control Map
Maps control over actions, signals, constraints, permissions, metrics, verification points, escalation paths, and deployment decisions.
In development
Human Oversight Reality Check
Tests whether oversight has time, competence, authority, source access, and the protected ability to pause or intervene.
In development
Workflow Boundary Map
Defines where the workflow begins, where AI enters, what decisions it shapes, and where consequences land.
In development
Verification Gate Builder
Designs necessary friction into AI-enabled workflows so optimization cannot outrun verification.
In development
Framework Exploration Map
A guided map for moving through HAOP concepts, performers, failure signatures, controls, and implementation tools without flattening the framework into a single page.
BOOK IN DEVELOPMENT
Forthcoming
A book on AI deployment risk through the lens of occupational and operational safety.
A broader examination of AI deployment risk through occupational and operational safety: how organizations use AI to accelerate work, compress judgment, scale weak signals, and move consequences faster than human adaptation can absorb.
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contact@haop.ai
For more information, speaking, collaboration, early review, or discussion of AI-enabled EHS, operational risk, human oversight, verification gates, and accountability-by-control.