  It is a well-known fact that current AI-based language technology -- language
models, machine translation systems, multilingual dictionaries and corpora --
focuses on the world's 2-3% most widely spoken languages. Recent research
efforts have attempted to expand the coverage of AI technology to
`under-resourced languages.' The goal of our paper is to bring attention to a
phenomenon that we call linguistic bias: multilingual language processing
systems often exhibit a hardwired, yet usually involuntary and hidden
representational preference towards certain languages. Linguistic bias is
manifested in uneven per-language performance even in the case of similar test
conditions. We show that biased technology is often the result of research and
development methodologies that do not do justice to the complexity of the
languages being represented, and that can even become ethically problematic as
they disregard valuable aspects of diversity as well as the needs of the
language communities themselves. As our attempt at building diversity-aware
language resources, we present a new initiative that aims at reducing
linguistic bias through both technological design and methodology, based on an
eye-level collaboration with local communities.
