  Autonomous agents acting in the real-world often operate based on models that
ignore certain aspects of the environment. The incompleteness of any given
model -- handcrafted or machine acquired -- is inevitable due to practical
limitations of any modeling technique for complex real-world settings. Due to
the limited fidelity of its model, an agent's actions may have unexpected,
undesirable consequences during execution. Learning to recognize and avoid such
negative side effects of an agent's actions is critical to improve the safety
and reliability of autonomous systems. Mitigating negative side effects is an
emerging research topic that is attracting increased attention due to the rapid
growth in the deployment of AI systems and their broad societal impacts. This
article provides a comprehensive overview of different forms of negative side
effects and the recent research efforts to address them. We identify key
characteristics of negative side effects, highlight the challenges in avoiding
negative side effects, and discuss recently developed approaches, contrasting
their benefits and limitations. The article concludes with a discussion of open
questions and suggestions for future research directions.
