Posts tagged "Risk-Assessment"

As AI systems become more sophisticated and autonomous, understanding how they interact with humans in complex, real-world scenarios becomes critical for safety. Traditional testing methods often miss edge cases that emerge from the messy reality of human behavior, environmental pressures, and system interactions. The Gap in AI Safety Testing Current AI safety approaches tend to focus on: Technical robustness (adversarial inputs, model failures) Alignment problems (reward hacking, goal specification) Controlled environments (laboratory testing, synthetic datasets) While these are essential, they often miss a crucial dimension: how AI systems behave when embedded in complex human systems under real-world constraints.