Addressing the opacity of black-box AI requires shifting from traditional fault-based liability to frameworks focused on risk management and transparency. Because identifying the exact point of failure in a complex neural network is often technically impossible, regulators are increasingly exploring strict liability models for high-risk applications. This approach holds developers or operators accountable for outcomes regardless of whether a specific error can be traced to a single line of code.
Furthermore, standards for Explainable AI (XAI) should be mandated. Regulations such as the EU AI Act suggest that systems performing critical functions must provide meaningful information about their decision-making logic. Establishing rigorous documentation and auditability standards ensures that even if a specific internal process is opaque, the inputs, training data, and intended functions are fully transparent to regulators.
Finally, a tiered regulatory approach is essential. Low-risk AI may require minimal oversight, while high-risk systems should be subject to mandatory third-party audits and continuous monitoring. By combining strict liability for high-stakes sectors with requirements for algorithmic traceability, legal systems can bridge the gap between technical complexity and legal accountability."
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