Ethical Challenges in Data-Driven Dialogue Systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, Joelle Pineau
TL;DR
Ethical challenges in data-driven dialogue systems arise because these models learn from biased and noisy data, leading to biased and unsafe outputs. The paper surveys key issues—bias, adversarial susceptibility, privacy leakage, safety, RL-specific concerns, and reproducibility—grounded in examples and targeted experiments. It provides preliminary measurements on bias in popular datasets and demonstrates privacy vulnerabilities via elicitation experiments, alongside discussing adversarial perturbations in generated text. The authors propose directions for mitigation, including debiasing, privacy-preserving training, robust evaluation, and reproducible research practices to advance safe and trustworthy dialogue technologies.
Abstract
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.