Assigning blame for a "black box" error requires moving away from the idea of finding a single person to punish. Instead, we need a multi-layered accountability model. Since developers cannot always explain exactly how a deep learning model reached a specific conclusion, we should focus on process-based liability. This means holding companies accountable for the quality of their training data and the rigors of their testing protocols.
From a clinical standpoint, the physician remains the final gatekeeper. If a doctor follows an AI recommendation blindly without verifying it against standard medical signs, they bear primary responsibility. However, if the AI functions as a decision-support tool that works as intended but fails due to an unpredictable internal logic, the liability shifts toward the manufacturer under product liability laws.
We also need mandatory transparency standards. Regulators should require companies to implement
