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Algorithmic Fairness in AI-Mediated Institutional Communication: A Computational Framework for Multilingual Professional Interaction (SpringerBriefs in Computer Science) - Tapa blanda
Sinopsis
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.
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Acerca del autor
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.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Algorithmic Fairness in AI-Mediated Institutional Communication
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 63 pp. Englisch. Nº de ref. del artículo: 9783032286574
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Algorithmic Fairness In Ai-Mediated Institutional Communication: A Computational Framework For Multilingual Professional Interaction
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783032286574
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -As Large Language Models increasingly shape professional discourselegal proceedings, cross-border documentation, and professional educationquestions 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.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 80 pp. Englisch. Nº de ref. del artículo: 9783032286574
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Taschenbuch. Condición: Neu. Algorithmic Fairness in AI-Mediated Institutional Communication | A Computational Framework for Multilingual Professional Interaction | Ran Yi (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | xv | Englisch | 2026 | Springer | EAN 9783032286574 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 136143238
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