Case Study: Deontological Ethics in NLP
This work provides a foundational ethical framework for NLP practitioners, though it is incremental as it applies existing ethical theory to the field.
The paper addresses the lack of discussion on ethical foundations in NLP by applying deontological ethics, specifically the generalization principle and respect for autonomy, through four case studies to demonstrate their use and recommend directions to avoid ethical issues.
Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices. However, there has been little discussion about the ethical foundations that underlie these efforts. In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP. In particular, we focus on the generalization principle and the respect for autonomy through informed consent. We provide four case studies to demonstrate how these principles can be used with NLP systems. We also recommend directions to avoid the ethical issues in these systems.