Book Chapter Published

Abstract

This chapter examines the integration of artificial intelligence (AI) technologies into corpus-based translation and interpreting studies (CTIS), exploring both the innovations it brings and the challenges it poses. The authors highlight how AI, particularly generative AI and machine-learning, can enhance methodological approaches in CTIS by automating data collection, corpus compilation, annotation, and linguistic feature extraction. They discuss how these advancements address traditional challenges in CTIS, such as limited access to data and labour-intensive processes. The chapter also reviews the application of AI in related fields, noting its potential to process complex and non-standard linguistic data. However, the authors caution against overreliance on AI, pointing out issues related to data limitations, accuracy, reliability, and potential biases inherent in AI outputs. They emphasise the need for a balanced integration of AI and human expertise, advocating for critical reflection on human–AI interactions to maximise the benefits while mitigating risks. The chapter concludes by underscoring the transformative potential of AI in advancing CTIS and calls for collaborative approaches to harness its capabilities effectively.