Abstract

Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, focusing on the challenges of ensuring safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance