چكيده لاتين
Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation is a multifaceted, interdisciplinary work that examines the diverse dimensions of responsibility in machine translation from technical, ethical, social, environmental, and legal perspectives. The book collects research studies, viewpoints, and case studies that each, in their own way, attempt to strike a balance between technological advancement and the observance of professional ethical principles in machine translation. The editors’ main aim is to show that machine translation is no longer merely a technical tool but a social and cultural phenomenon that directly affects people, data, and communities.
The opening chapters probe the concept of “responsibility in machine translation” from historical and theoretical standpoints. Tracing the evolution of language models from statistical systems to neural networks, the authors draw attention to issues such as algorithmic transparency, data bias, and users’ overreliance on automated outputs. This section demonstrates that technological progress—if pursued without ethical and social oversight—can foster distrust and even reproduce discrimination in translation workflows. Accordingly, the authors stress the necessity of retaining human judgment and oversight at critical points in the translation process.
Middle sections of the book address topics such as licensing and data ownership, the role of human post-editors, and the end-user perspective. The contributors argue that clarifying data provenance and safeguarding content owners’ rights are preconditions for responsible system development. They also discuss the professional and ethical challenges faced by human post-editors, including diminished autonomy, cognitive fatigue, and time pressure associated with editing machine-generated translations. From the end-user perspective, the book shows that users can trust MT outputs only if they are sufficiently informed about how the outputs were produced and about the technology’s limitations.
Subsequent chapters explore more specific and high-stakes concerns, including ethics in crisis situations, where the authors examine the use of MT in humanitarian emergencies, armed conflict, and natural disasters. In such contexts, even minor system errors can have severe consequences for people’s safety; therefore, MT deployment should be accompanied by human mediation and multi-layered validation. Another chapter focuses on gender bias, demonstrating how imbalanced training data and algorithmic design choices can reproduce gender stereotypes. The authors recommend design and training strategies that prioritize equity and balanced representation of gender groups.
A notable contribution of the book is the chapter on the ecological footprint of large MT models, which analyzes the environmental costs of training and deploying large-scale neural systems. Drawing on empirical data, the authors show that large model training consumes substantial energy and natural resources. They advise researchers and industry to pursue more energy-efficient architectures, model compression techniques, and the use of low-carbon infrastructures. This section frames environmental responsibility as an integral component of professional ethics in machine translation research and practice.
The final chapter, Speech as Personally Identifiable Information (PII), highlights privacy and security concerns related to voice data. Given the increasing deployment of speech-enabled translation technologies, new ethical and legal questions arise about storing and analyzing users’ voices. The authors call for explicit policies to protect personal speech data and to respect users’ right to anonymity.
