The debate around AI safety has too often lived in the realm of philosophy. As AI systems take on real roles in global infrastructure, that is no longer good enough. Safety must be measurable, reproducible, and grounded in engineering practice — the same way we hold bridges, aircraft, and power grids to account.
Our CEO, Dr. Saurabh Mishra, recently co-authored a compelling analysis for OECD.AI exploring how classical Reliability Engineering principles can be rigorously applied to modern AI systems. Working alongside Amin Aria and Andrea Renda, the piece outlines a proactive, data-driven approach to risk management that the field has long needed.
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The elegance of this proposal lies in its pragmatism. Rather than inventing new frameworks from scratch, it applies time-tested engineering metrics to the challenge of AI. By treating AI models as complex systems — because they are — we can anticipate and manage potential failures before they cause harm. The "Bathtub Curve" allows analysts to examine failure rates across a system's lifecycle, identifying early-stage vulnerabilities before they reach users. MTTF and MTBF metrics — Mean Time to Failure and Mean Time Between Failures — create predictable benchmarks for AI uptime and safety that mirror the standards already used in critical physical infrastructure. And by incorporating human reliability into the analysis, the framework ensures that AI-assisted decision-making remains resilient under pressure, not just in controlled conditions.
Aligning with Global Regulation
This framework arrives at exactly the right moment. As international regulatory bodies move toward more structured oversight, having a principled engineering foundation makes compliance tangible rather than theoretical. The principles align directly with several emerging standards: the NIST AI Risk Management Framework, the EU AI Act, Singapore's AI Verify, and the OECD's Catalogue of Tools and Metrics. Rather than treating these frameworks as bureaucratic requirements, the reliability engineering approach gives organisations a genuine technical basis for meeting them.
Advancing AI Safety at Taiyo.AI
At Taiyo.AI, these are not abstract principles — they are how we build. For AI to be a viable tool in the construction and infrastructure sectors, it must meet the same rigorous safety standards as the physical structures it helps manage. By grounding our work in reliability engineering, we ensure that the insights we deliver are not only accurate but consistently dependable and safe for long-term deployment.
"We propose using foundational Reliability Engineering principles to help guide engineers and policymakers... incorporating aspects of human reliability to inform a multi-disciplinary and data-driven framework." — Saurabh Mishra
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