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Development of speech and language technology for social good (LT4SG),especially those targeted at the welfare of marginalized communities andspeakers of low-resource and under-served languages, has been a prominent themeof research within NLP, Speech, and the AI communities. Researchers have mostlyrelied on their individual expertise, experiences or ad hoc surveys forprioritization of language technologies that provide social good to theend-users. This has been criticized by several scholars who argue that work onLT4SG must include the target linguistic communities during the design anddevelopment process. However, none of the LT4SG work and their critiquessuggest principled techniques for prioritization of the technologies andmethods for inclusion of the end-user during the development cycle. Drawinginspiration from the fields of Economics, Ethics, Psychology, and ParticipatoryDesign, here we chart out a set of methodologies for prioritizing LT4SG thatare aligned with the end-user preferences. We then analyze several LT4SGefforts in light of the proposed methodologies and bring out their hiddenassumptions and potential pitfalls. While the current study is limited tolanguage technologies, we believe that the principles and prioritizationtechniques highlighted here are applicable more broadly to AI for Social Good.

Designing Language Technologies for Social Good: The Road not Taken