Algorithmic Fairness in AI-Mediated Institutional Communication: A Computational Framework for Multilingual Professional Interaction (SpringerBriefs in Computer Science) - Softcover

Yi, Ran; Li, Zhensheng; Zhang, Wei

Softcover

ISBN 10: 3032286573 ISBN 13: 9783032286574

Publisher: Springer, 2026

Synopsis

As Large Language Models increasingly shape professional discourse—legal proceedings, cross-border documentation, and professional education—questions of linguistic equity and algorithmic accountability become urgent. The book develops a computational framework for evaluating fairness in AI-mediated institutional communication. 

The book introduces a transformer-based benchmarking architecture designed to measure communicative competence and fairness across multilingual institutional settings. Using domain-specific corpora from cross-border professional environments, it operationalises sociolinguistic indicators into measurable computational metrics.

Through model validation, bias analysis, and cross-lingual robustness testing, the authors demonstrate how fairness in professional communication can be evaluated beyond generic NLP benchmarks, and propose a replicable framework for integrating linguistic justice principles into AI system assessment. This book will be of interest to researchers in NLP fairness, computational sociolinguistics, multilingual AI systems, and applied machine learning in institutional domains.

About this title

About the Author

Ran Yi is an Associate Professor at the National University of Defense Technology, China. Her research integrates computational linguistics, legal discourse analysis, and multilingual NLP. She studies algorithmic fairness and institutional communication, developing discourse-aware evaluation frameworks for AI-mediated professional language technologies in global settings.

Zhensheng Li is a researcher at Shanghai AI Laboratory working on large language models, multilingual representation learning, and trustworthy AI systems. His work focuses on cross-lingual modeling, efficient training architectures, and fairness evaluation for language technologies deployed in complex real-world
institutional environments today globally.

Wei Zhang is an Associate Professor at the National University of Defense Technology, China. His research focuses on institutional discourse, policy communication, and professional language in regulated contexts. He studies institutional discourse norms, authority, and interpretability. 

 

From the Back Cover

As Large Language Models increasingly shape professional discourse—legal proceedings, cross-border documentation, and professional education—questions of linguistic equity and algorithmic accountability become urgent. The book develops a computational framework for evaluating fairness in AI-mediated institutional communication. 

The book introduces a transformer-based benchmarking architecture designed to measure communicative competence and fairness across multilingual institutional settings. Using domain-specific corpora from cross-border professional environments, it operationalises sociolinguistic indicators into measurable computational metrics.

Through model validation, bias analysis, and cross-lingual robustness testing, the authors demonstrate how fairness in professional communication can be evaluated beyond generic NLP benchmarks, and propose a replicable framework for integrating linguistic justice principles into AI system assessment. This book will be of interest to researchers in NLP fairness, computational sociolinguistics, multilingual AI systems, and applied machine learning in institutional domains.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 3032286573
• ISBN 13: 9783032286574
• Binding: Paperback
• Number of pages: 78