A Material Lens on Coloniality in NLP
William Held Camille Harris Michael Best Diyi Yang
Coloniality, the continuation of colonial harms beyond "official"
colonization, has pervasive effects across society and scientific fields.
Natural Language Processing (NLP) is no exception to this broad phenomenon. In
this work, we argue that coloniality is implicitly embedded in and amplified by
NLP data, algorithms, and software. We formalize this analysis using
Actor-Network Theory (ANT): an approach to understanding social phenomena
through the network of relationships between human stakeholders and technology.
We use our Actor-Network to guide a quantitative survey of the geography of
different phases of NLP research, providing evidence that inequality along
colonial boundaries increases as NLP builds on itself. Based on this, we argue
that combating coloniality in NLP requires not only changing current values but
also active work to remove the accumulation of colonial ideals in our
foundational data and algorithms.