Method — AI Liability

Definition, scope boundary, and structural model.

Definition

AI liability describes a structural framework for allocating responsibility, accountability, and risk arising from the development, deployment, and operation of artificial intelligence systems.

It operates across system boundaries, linking actions, decisions, and outcomes to accountable entities without prescribing legal interpretation or enforcement mechanisms.

Model Classification

AI liability is structured as a descriptive and analytical reference model.

It provides a framework for mapping responsibility and accountability across system components without prescribing normative judgments, legal interpretation, or operational actions.

Scope Boundary

Included

Allocation of responsibility across AI system lifecycle stages
Attribution of outcomes to system actors and roles
Risk distribution between developers, operators, and users
Linkage between system behavior and accountable entities
Structural mapping of liability across complex system interactions

Excluded

Legal advice or jurisdiction-specific liability interpretation
Regulatory enforcement or compliance certification
Case-specific liability assessment or adjudication
Implementation of legal frameworks or contractual structures
Vendor-specific liability models or insurance products

Structural Phase Model

Phase 1 — System Creation

Artificial intelligence systems are designed, developed, and configured, establishing initial responsibility boundaries.

Phase 2 — Deployment Context

Systems are introduced into operational environments, defining the roles and responsibilities of involved entities.

Phase 3 — Operational Behavior

System actions and decisions occur within defined contexts, generating outcomes that may require attribution.

Phase 4 — Responsibility Attribution

Outcomes are linked to accountable entities based on system roles, interactions, and structural relationships.

Transferability

The structural model of AI liability is not limited to artificial intelligence systems.

It can be applied to any system in which outcomes must be attributed across multiple actors, including software systems, autonomous agents, organizational processes, and hybrid human-machine environments.

The model remains consistent across contexts by focusing on structural relationships between actions, outcomes, and accountable entities.