Algorithmic Fairness in AI-Mediated Institutional Communication

A Computational Framework for Multilingual Professional Interaction
Erscheinungsjahr 2026
ISBN: 978-3-032-28657-4
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 63 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: SpringerBriefs in Computer Science

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Yi, Ran

Li, Zhensheng

Zhang, Wei

Fachgebiete

• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung

Inhaltsverzeichnis

Dedication.- Acknowledgements.- Preface.- 1. Introduction.- 2. Theoretical Foundations: From Sociolinguistics to Computational Fairness.- 3. Corpus Design and Annotation Framework.- 4. Transformer-Based Benchmark Architecture.- 5. Fairness and Bias Evaluation.- 6. Implications for Institutional AI Governance.- 7. Conclusion.

Über Autor(innen)

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.