  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.
