Algorithmic Fairness in AI-Mediated Institutional Communication

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.