Reliability, Resilience and Human Factors Engineering for Trustworthy AI Systems

Traditional engineering reliability methods can be adapted to AI systems: The paper demonstrates that established metrics like failure rate and Mean Time Between Failures (MTBF)—long used in aerospace and other critical industries—can be applied to AI systems to measure and improve their dependability in real-world operations.

Human factors matter as much as technical ones: The framework integrates human reliability analysis alongside technical resilience engineering, recognizing that trustworthy AI systems require attention to how humans interact with and oversee these technologies, not just the algorithms themselves.

Practical application is already possible: The authors tested their framework using real system status data from platforms like OpenAI, showing that organizations can begin implementing these reliability principles now rather than waiting for perfect theoretical models.

This approach supports regulatory compliance: The framework aligns with emerging global standards and regulatory requirements for AI, offering a concrete methodology that policymakers and companies can use to demonstrate that AI systems are safe and trustworthy.

Recovery from failures is as important as prevention: Beyond just preventing AI failures, the framework emphasizes building resilience—the ability to efficiently recover when problems do occur—which is critical for AI systems operating in high-stakes environments like healthcare or infrastructure.