Ethical Challenges in Data-Driven Dialogue Systems

Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier, Nan Rosemary Ke, Genevieve Fried, Ryan Lowe, Joelle Pineau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

88 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
PublisherAssociation for Computing Machinery, Inc
Pages123-129
Number of pages7
ISBN (Electronic)9781450360128
DOIs
StatePublished - Dec 27 2018
Externally publishedYes
Event1st AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 3 2018

Publication series

NameAIES 2018 - Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

Conference

Conference1st AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018
Country/TerritoryUnited States
CityNew Orleans
Period2/2/182/3/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Keywords

  • Adversarial Examples
  • Bias
  • Computers and Society
  • Dialogue Systems
  • Ethics and Safety
  • Machine Learning
  • Natural Language Processing
  • Privacy
  • Reinforcement Learning
  • Reproducibility
  • Security

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