Ridwan Abdusalam’s work highlights a critical blind spot in modern AI hardware: systems can appear operational while silently producing incorrect inferences. By developing contamination-aware reliability testing and introducing metrics like perceptual uptime, he reframes how engineers evaluate trustworthy AI in vision-based devices. His framework aims to move industry standards beyond simple uptime toward continuous monitoring of real-world inference reliability, helping ensure AI-enabled hardware remains safe, accountable, and dependable at scale.