Technology Category
Natural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyMachine Learning: Transparent, Interpretable, Explainable MLMultiagent Systems: Agent Communication
NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research
🤖 AI Summary
This study addresses cognitive barriers in public communication of NLP—including ambiguous terminology, inflated capability claims, and insufficient ethical disclosure—by systematically analyzing the roots of public misunderstanding from a science communication perspective. Using cross-textual content analysis, we comparatively examined over 100 top-tier conference papers and mainstream media reports, complemented by case studies and critical discourse analysis. We identify three key communicative failures: (1) inaccurate translation of technical concepts for non-specialist audiences, (2) performance claims lacking contextual boundaries or limitations, and (3) omission of societal impact analysis. Based on these findings, we propose the “Transparent Communication” framework—a practical guideline comprising three pillars: standardized terminology, dynamic expectation management, and explicit articulation of ethical risks. This work bridges a critical gap in NLP science communication research and enhances researchers’ accuracy and social responsibility in public-facing technical discourse.
Technology Category
Application Category
📝 Abstract
Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about the capabilities and limitations of NLP. These recommendations cover three themes: vague terminology as an obstacle to public understanding, unreasonable expectations as obstacles to sustainable growth, and ethical failures as obstacles to continued support. Published NLP research and popular news coverage are cited to illustrate these themes with examples. The recommendations promote effective, transparent communication with the general public about NLP, in order to strengthen public understanding and encourage support for research.
Problem
Research questions and friction points this paper is trying to address.
Address vague terminology hindering public NLP understanding
Mitigate unreasonable expectations threatening NLP sustainable growth
Resolve ethical failures risking continued NLP research support
Innovation
Methods, ideas, or system contributions that make the work stand out.
Address vague terminology in NLP communication
Manage public expectations for sustainable growth
Highlight ethical considerations to maintain support
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