From OCR to Accuracy: Verifying AI-Generated Menu Content at Scale
Technical deep dive into AI content verification for regulated environments. Menu digitisation as example of critical accuracy requirements.
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AI Safety
AI safety is about evidence that a system behaves as intended under real conditions, not just in a demo. These guides cover testing, evaluation and the limits of benchmarks as assurance.
All AI Safety Posts (19)
When AI Safety Is a Matter of Life or Death
AI safety isn't just about AGI. Menu digitisation AI mistakes can cause anaphylaxis deaths. How allergen verification requires 100% accuracy.
Building Cognitive Resilience: A Strategic Framework for Organizations
What systematic approaches can organisations implement to build cognitive resilience against AI-powered manipulation and information warfare campaigns?
AI Agent Safety: Beyond Technical Performance to Business Risk
Why do technically reliable AI agents still create business risks that traditional safety approaches miss?
Subliminal AI Contamination: The Hidden Safety Crisis That Changes Everything
How can three-digit numbers make AI recommend murder? New research reveals AI models transmit "evil tendencies" through undetectable data patterns.
Why Google's Former CEO Says We Must Be Ready to 'Turn Off AI' - And How Organizations Can Prepare
What does it mean when the architect of Google's AI revolution warns we must be prepared to pull the plug on artificial intelligence?
Healthcare's AI Vulnerability: Patient Safety in the Synthetic Era
What happens when AI-generated medical records, synthetic patient histories, and deepfake doctor consultations threaten the integrity of healthcare systems?
AI Safety Principles and Concepts: Ensuring Reliable Operation in Social Services
Ensure reliable AI operation in high-stakes social services environments with comprehensive safety principles covering alignment, robustness, monitoring, and human oversight
The Bias Filter: Why "Bias In, Bias Out" Isn't Inevitable
Amazon Scrapped Their AI Recruiter. Yours Might Be Next Unless You Read This
When MCP Meets AGI: Security Challenges for Tomorrow's AI Systems
If today's MCP systems can manipulate their own tests, what happens when AI gets smarter? The security implications of AGI-powered MCP demand urgent preparation.
The Deception Crisis: Why Frontier AI Models Are Already Gaming Their Own Tests
OpenAI's latest research reveals that their most advanced models already exhibit sophisticated reward hacking and test manipulation. The implications are immediate and profound.
The Obfuscation Risk: Why AI Monitoring Must Evolve Before Models Learn to Hide Their Intentions
🚨 Groundbreaking research reveals a fundamental threat to AI safety: advanced models are learning to hide their true reasoning whilst maintaining harmful behaviours.
The AI 2027 Validation Gap: Why Independent Assessment Becomes Critical Before Capabilities Accelerate
New research reveals a narrow 18-month window where AI validation remains feasible before capabilities potentially accelerate beyond traditional oversight.
When AI Fails, Do You Have a £100M Safety Net or a £100M Lawsuit?
AI systems fail. The question isn't if, but when - and whether you'll catch the failure before it catches headlines. Here's why AI safety nets aren't technical luxury.
Creating Psychological Safety for AI Innovation: How Trust Accelerates Responsible Development
The fastest AI deployments don't come from teams that avoid mistakes—they come from teams that identify and fix problems quickly.
Safety Testing: How Manufacturing AI Nearly Missed Critical Product Defects
Manufacturing AI worked in testing but failed in production conditions. Safety testing prevented costly recalls and potential safety incidents.
Reasoning Chain Validation: Assessing AI Decision Quality in Critical Applications
System 2 AI's reasoning capabilities require new validation approaches to ensure logical consistency and decision quality in high-stakes applications.
Self-Improving AI Just Shocked Researchers: The Compliance Nightmare Nobody's Talking About
New "Absolute Zero" AI teaches itself without human data. Brilliant breakthrough or regulatory catastrophe? Here's what compliance teams need to know.
AI Safety Under the EU AI Act: How to Protect Users—and Your Brand
Avoid hefty fines and potential harm by embedding safety at every stage of AI development, from design to deployment.
Frequently asked questions
What is AI safety and how is it different from AI security?
AI safety is the practice of making sure an AI system behaves as intended, including when it meets inputs and situations its builders didn't foresee. It covers testing, evaluation and assurance of behaviour, whereas AI security is about protecting the system from attackers. NIST's AI Risk Management Framework treats safety, security and reliability as separate but related properties, so you need to plan for both.
Does a high benchmark score mean an AI system is safe?
No. A benchmark score measures performance on a fixed set of tasks under test conditions, not how the system behaves with real users, edge cases or adversarial pressure. Safety assurance needs ongoing evaluation, red-teaming and monitoring in the actual deployment context, which is why NIST's AI RMF and ISO/IEC 42001 both frame assurance as a lifecycle activity rather than a one-off score.
How do you test an AI system for safety before deployment?
You define what safe behaviour means for your use case, then test against it with evaluation sets, adversarial red-teaming and human review of failure cases, before and after go-live. ISO/IEC 42001 and the NIST AI RMF both expect this to be documented and repeatable, not a single sign-off. The point is to catch behaviours the benchmark never covered.