Case Study: Deontological Ethics in NLP

TL;DR

This paper explores the application of deontological ethics, focusing on the generalization principle and respect for autonomy, to NLP systems through four case studies, aiming to guide ethical system design.

Contribution

It introduces a framework for applying deontological ethics in NLP, emphasizing ethical principles like informed consent and generalization, with practical case studies and recommendations.

Findings

Four case studies demonstrating ethical principles in NLP

Guidelines for ethical NLP system development

Identification of key ethical challenges in NLP

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.