Case Study: Deontological Ethics in NLP
Shrimai Prabhumoye, Brendon Boldt, Ruslan Salakhutdinov, Alan W Black
Abstract
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.
BibTeX
@inproceedings{prabhumoye-etal-2021-case,
title = "Case Study: Deontological Ethics in {NLP}",
author = "Prabhumoye, Shrimai and
Boldt, Brendon and
Salakhutdinov, Ruslan and
Black, Alan W",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.297/",
doi = "10.18653/v1/2021.naacl-main.297",
pages = "3784--3798"
}