  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.
