Ethical Challenges in Data-Driven Dialogue Systems

TL;DR

This paper surveys ethical challenges in data-driven dialogue systems, highlighting biases, privacy issues, safety concerns, and reproducibility problems to promote development of more ethical and robust conversational AI.

Contribution

It provides a comprehensive overview of ethical issues in dialogue systems and identifies key areas for future research to address these challenges.

Findings

Biases and offensive outputs in dialogue systems due to training data

Privacy violations and safety concerns in deployment

Reproducibility issues in research and development

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.