Human-AI Governance (HAIG) frameworks resolve accountability gaps by shifting the focus from the autonomous agent to the human ecosystem surrounding the system. Since AI lacks legal personhood and moral agency, frameworks establish a clear chain of responsibility through principles of human oversight and traceability.
To address legal accountability, HAIG implements strict liability or negligence models. This ensures that developers, deployers, or operators are held responsible based on their level of control and the foreseeability of the risk. By maintaining detailed audit trails and technical logs, these frameworks allow investigators to reconstruct decision-making processes, identifying whether the harm resulted from faulty design, inadequate training data, or improper human intervention.
Regarding moral accountability, HAIG emphasizes the principle of meaningful human control. This requires that humans remain involved in critical decision points to ensure ethical alignment. When unintended harm occurs, the framework facilitates a distributed responsibility model where accountability is assigned to the human stakeholders who profited from or supervised the system. This structure prevents the accountability gap by ensuring that no consequence occurs without a corresponding human or corporate obligation to provide remedy.
