Machine Learning Safety

Machine Learning Safety refers to practices and principles that ensure artificial intelligence systems behave reliably and ethically. As AI models learn from data, they can make mistakes or produce biased results. Safety measures aim to prevent harmful outcomes, provide transparency, and ensure human oversight in AI decision-making. This includes rigorously testing models, understanding their limitations, and implementing guidelines to handle unexpected behaviors. By prioritizing safety, we strive to harness the benefits of AI while minimizing risks to individuals and society.