Designing Language Technologies for Social Good: The Road not Taken
This work tackles the problem of improving the design and prioritization of language technologies for marginalized communities and low-resource languages, offering a framework that could be applied broadly to AI for social good, though it is incremental in building on existing critiques.
The paper addresses the lack of principled methods for prioritizing language technologies for social good (LT4SG) and involving end-users, proposing methodologies inspired by fields like Economics and Participatory Design to align with user preferences and analyzing existing efforts to reveal hidden assumptions and pitfalls.
Development 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. Researchers have mostly relied on their individual expertise, experiences or ad hoc surveys for prioritization of language technologies that provide social good to the end-users. This has been criticized by several scholars who argue that work on LT4SG must include the target linguistic communities during the design and development process. However, none of the LT4SG work and their critiques suggest principled techniques for prioritization of the technologies and methods for inclusion of the end-user during the development cycle. Drawing inspiration from the fields of Economics, Ethics, Psychology, and Participatory Design, here we chart out a set of methodologies for prioritizing LT4SG that are aligned with the end-user preferences. We then analyze several LT4SG efforts in light of the proposed methodologies and bring out their hidden assumptions and potential pitfalls. While the current study is limited to language technologies, we believe that the principles and prioritization techniques highlighted here are applicable more broadly to AI for Social Good.