Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of ve practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function (avoiding side effects" and avoiding reward hacking"), an objective function that is too expensive to evaluate frequently (scalable supervision"), or undesirable behavior during the learning process (safe exploration" and distributional shift"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI.
- Full paper here