The Six Conundrums of Building and Deploying Language Technologies for Social Good

Abstract

Deployment of speech and language technology for social good (LT4SG), especially those targeted at the welfare of marginalized communities and speakers of low-resource and under-served languages, has been a prominent theme of research within NLP, Speech and the AI communities. Many researchers, especially those working in core NLP/Speech domains, rely on a combination of individual expertise, experiences or ad hoc surveys for prioritizing between language technologies that provide social good to the end-users. This has been criticized by several scholars who argue that it is critical to include the target community during the LT’s design and development process. However, prioritization of communities, languages, technologies and design approaches presents a very large set of complex challenges to the technologists, for which there are no simple or off-the-shelf solutions. In this position paper, we distill our experiential insights into six fundamental conundrums that technologists face and must resolve while deciding which LT technology to build for which community, and by using what approach. We discuss that at the root of these conundrums lie certain fundamental ethical problems of a digital-divide that can be overcome only by resolving deeper ethical dilemmas of distributive justice. We urge the community to reflect on these conundrums and leverage shared experiential insights to reconcile the intent of broadly, any Technology for Social Good, with the ground realities of its deployment.