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This paper investigates ethical considerations in NLP, focusing on bias, privacy, and misinformation, which are increasingly important as NLP systems are deployed more widely. The authors apply the Value Sensitive Design (VSD) framework to analyze the ethical implications of NLP algorithms, particularly in the context of knowledge graphs. The paper aims to enhance the ethics of NLP solutions by exploring the intersections between NLP and knowledge graphs through the lens of VSD.
Value Sensitive Design offers a structured approach to bake ethics into NLP systems from the start, rather than as an afterthought.
Natural Language Processing (NLP) stands at the forefront of artificial intelligence, empowering machines with human-like capabilities to process text and speech. The versatile applications of NLP encompass information retrieval, text summarization, text categorization, and machine translation. In the dynamic landscape of information dissemination, the prevalence of misinformation, bias and the need for credible sources have become critical concerns. This research effort addresses critical ethical considerations related to the development of NLP algorithms using the Value Sensitive Design (VSD) framework. This paper explores the intersections between NLP and knowledge graphs, through the lens of VSD aiming to enhance the ethics of NLP solutions.
