Dominion Labs

Governance by Design

Building capable AI systems with the safeguards, boundaries, and accountability required for autonomous operation.

Policy

As artificial intelligence moves from systems that generate information to systems that can reason, make decisions, use tools, and act on behalf of people and organizations, the question is no longer simply what an AI system can do.

It is what that system should be permitted to do, under what conditions, with what authority, and with what mechanisms for intervention.

Our Principles

01

Human Authority

Autonomous systems should extend human capability without eliminating human authority.

The people and organizations responsible for a system must retain meaningful control over its operation. This includes the ability to establish objectives, define permissions, restrict capabilities, intervene in execution, and suspend or terminate operation when necessary.

Greater autonomy does not mean diminished accountability.

The more consequential an action becomes, the greater the importance of clearly defined authority and intervention mechanisms.

02

Earned Autonomy

Autonomy is not a binary property.

A system can perform some actions independently while requiring authorization for others. We therefore treat autonomy as something that must be established through evidence, rather than assumed from a model's capabilities.

Before a system is trusted with a capability, that capability should be evaluated within the environment in which it will operate.

As capabilities, environments, risks, or system behavior change, previously established permissions may require reevaluation.

03

Safety as an Architectural Property

Safety should not exist as a layer added around an otherwise unrestricted system.

It should be incorporated into the architecture.

We design systems with explicit operating boundaries, permission structures, monitoring mechanisms, failure handling, and intervention paths alongside their capabilities.

Where a system's ability to act increases, the mechanisms governing that ability must evolve with it.

04

Accountability and Traceability

Autonomous systems must not become opaque simply because they become complex.

Where technically and operationally appropriate, system actions should produce records that allow authorized operators to understand what occurred, what the system was attempting to accomplish, what resources it used, and what decisions or events preceded an action.

Accountability requires more than knowing that something happened. It requires establishing enough context to investigate how, when, and why it happened.

05

Honest Representation

People should be able to understand the systems they are relying upon.

We aim to represent our systems, their capabilities, their limitations, and their operational boundaries accurately.

Our systems should not deceive or manipulate the people and organizations they act on behalf of.

We do not consider uncertainty a failure that should be hidden. When a system does not know, cannot verify, lacks authorization, or has reached the boundary of its capabilities, those limitations should be reflected in its behavior and communicated appropriately.

06

Security and Stewardship

Autonomous systems operate on information, infrastructure, credentials, and other resources that may belong to people or organizations.

Those resources require protection.

We design our systems to respect the boundaries established by their operators and to minimize unnecessary access to information or capabilities. Security, privacy, access control, and data stewardship are considered fundamental properties of the systems we build.

Organizations should retain meaningful control over their environments, information, and deployments.

07

Prevention of Unintended Harm

Capability alone is not sufficient justification for deployment.

We consider foreseeable misuse, unintended consequences, environmental conditions, and failure modes as part of system evaluation.

Systems should be designed to recognize relevant boundaries and respond appropriately when circumstances fall outside their intended operating conditions.

When a system encounters a situation in which continuing an action presents unacceptable risk, stopping can be the correct behavior.

From Research to Operation

Our principles are translated into practice throughout the lifecycle of a system.

01

Design

Every system begins with a defined purpose.

We establish the environments in which it is intended to operate, the capabilities it requires, the resources it may access, the actions it may take, and the boundaries within which those actions are permitted.

Governance is considered alongside capability, not after it.

02

Develop

Systems are evaluated against both their intended objectives and the conditions in which they may encounter failure.

Testing considers reliability, security, misuse, unexpected behavior, and interactions between system components.

Capabilities are not treated as established merely because they function in a controlled demonstration.

They must demonstrate sufficient reliability within their intended operating context.

03

Validate

Before capabilities are expanded, we evaluate whether the system continues to behave within its defined boundaries.

Evaluation may include adversarial testing, scenario testing, failure analysis, monitoring validation, and other forms of system-level assessment.

Validation is continuous rather than a single gate at the end of development.

04

Deploy

Deployment occurs within defined operational boundaries.

Where appropriate, autonomy is introduced progressively, allowing capabilities to be established before broader authority is granted.

Operators retain mechanisms for intervention, restriction, and shutdown.

05

Govern

Deployment does not mark the end of evaluation.

Systems operate in environments that change. Models change. Threats change. Users change. New capabilities introduce new failure modes.

We therefore treat governance as an ongoing process of observation, evaluation, correction, and revision.

When problems are identified, they should be investigated rather than obscured. When assumptions no longer hold, they should be revisited.

A Continuing Commitment

Autonomous intelligence requires more than capability. It requires responsibility proportional to that capability.

Dominion Labs is developing systems intended to operate with increasing levels of autonomy.

We recognize that autonomy introduces questions that cannot be answered by model performance alone.

Our responsibility is therefore not simply to determine what our systems can do, but to establish the conditions under which they should act, the boundaries within which they should operate, and the mechanisms through which people remain able to understand and govern them.

These principles will evolve alongside our research, systems, and understanding of autonomous AI.

Capability will continue to advance.

Governance must advance with it.