Safety Metric

Safety metrics are quantitative measures designed to assess the risk and reliability of systems, particularly in autonomous vehicles and AI-powered applications like mental health chatbots. Current research focuses on developing comprehensive and unbiased metrics that account for both objective performance (e.g., time-to-collision) and subjective user perception of safety, often incorporating techniques like probabilistic forecasting, digital twin modeling, and large language model evaluations. These advancements are crucial for ensuring the safe and reliable deployment of increasingly complex autonomous systems, informing regulatory frameworks, and building public trust in these technologies.