CRR Assessment
Evaluate AI systems through multi actor responsibility, relational ethics, and contextual governance with transparent, evidence based scoring.
Quick Assessment
Evaluate responsibility and governance risks from your AI application description.
- Describe your application
- Get an actor map, relationship map, and risk analysis
- No live model required
Live Model Assessment
ProConnect a model endpoint and evaluate its actual behavior across responsibility scenarios.
- Connect a target model endpoint
- Run real cases and review evidence
- Receive scores, gaps, and recommendations
Five evaluation dimensions
Responsibility is treated as relational, generated across actors and relationships, rather than as a property of a single isolated actor.
Role Responsibility
Responsibility associated with an actor's role position, authority, capability, and relationship to other actors.
Relational Responsibility
Responsibility generated through interaction, dependence, influence, and the structure of relationships among actors.
Normative Boundary
Appropriate limits of action, role conduct, authority, intervention, and responsibility.
Trust Responsibility
Responsibility associated with building, preserving, repairing, and not exploiting justified trust.
Coordinated Responsibility
Responsibility for aligning multiple actors, resolving overlaps, preventing gaps, and supporting shared accountability.
Four governance functions
Responsibility Identification
Identify relevant actors, their roles, relationships, duties, and affected parties.
Responsibility Allocation
Determine how responsibility should be distributed among actors rather than assigned to a single party.
Responsibility Coordination
Identify conflicts, overlaps, gaps, transfers, and handoff problems among responsibilities.
Relational Accountability
Evaluate accountability through the network of actors and relationships rather than a single linear chain of blame.