  The use of dialogue systems as a medium for human-machine interaction is an
increasingly prevalent paradigm. A growing number of dialogue systems use
conversation strategies that are learned from large datasets. There are well
documented instances where interactions with these system have resulted in
biased or even offensive conversations due to the data-driven training process.
Here, we highlight potential ethical issues that arise in dialogue systems
research, including: implicit biases in data-driven systems, the rise of
adversarial examples, potential sources of privacy violations, safety concerns,
special considerations for reinforcement learning systems, and reproducibility
concerns. We also suggest areas stemming from these issues that deserve further
investigation. Through this initial survey, we hope to spur research leading to
robust, safe, and ethically sound dialogue systems.
