Machine Translation (MT) has become a default language access tool in public and third sector organisations serving multilingual communities. However, how organisations, and their staff, actually use it, and their opinions about it, remain largely undocumented. This paper reports findings from charities, NGOs, community organisations and local government authorities in the West Midlands (UK), one of the most linguistically diverse areas of the country. The results indicate that MT use is widespread, informal, and driven by necessity rather than informed decisions or policies. Google Translate is the preferred tool; policies about MT use are rare, and confidence in translation quality is limited. Risk perception varies across the sector: local government respondents identify the widest range of concerns, including legal and medical, while third-sector organisations suggest a pragmatic approach. However, greater risk-awareness does not lead to greater governance, pointing to a gap between individual MT literacy and institutional accountability. Based on this, we propose some recommendations for how organisation serving multilingual communities should approach MT implementation and training.

The purpose of the current investigation is to predict post-editor profiles based on user behaviour and demographics using machine learning techniques to gain a better understanding of post-editor styles. Our study extracts process unit features from the CasMaCat LS14 database from the CRITT Translation Process Research Database (TPR-DB). The analysis has two main research goals: We create n-gram models based on user activity and part-of-speech sequences to automatically cluster post-editors, and we use discriminative classifier models to characterize post-editors based on a diverse range of translation process features. The classification and clustering of participants resulting from our study suggest this type of exploration could be used as a tool to develop new translation tool features or customization possibilities.