Machine learning (ML)  systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address.
We present four problems ready for research, namely withstanding hazards (“Robustness”) , identifying hazards (“Monitoring”) , steering ML systems (“Alignment”) , and reducing deployment hazards (“Systemic Safety”) . Throughout, we clarify each problem’s motivation and provide concrete research directions.