Babel Fish Democracy? Prospects for Addressing Democratic Language Barriers through Machine Translation and Interpretation

Abstract

This article works to show that machine translation and interpretation technologies can play significant roles in addressing language barriers in democratic polities, including beyond the state. Numerous shared-language or interpretation models have been proposed to address such barriers, while possible machine models have largely been dismissed in the literature. Yet, online translation is now ubiquitous globally, and governments and international organizations increasingly use machine translation and some interpretation applications. Such technologies, it is shown, can greatly expand information uptake and participation by ordinary citizens in linguistically diverse polities. They also can avoid key fairness and cost concerns faced by other models, or help address them in hybrid configurations, especially in online settings. While speech-to-speech applications may never achieve the seamless vernacular “Babel fish” interpretations implied as necessary for some deliberative modes, machine models can be seen as valuable for addressing language barriers within a range of approaches to democracy.