The "Colonial Impulse" of Natural Language Processing: An Audit of
  Bengali Sentiment Analysis Tools and Their Identity-based Biases
Dipto Das Shion Guha Jed Brubaker Bryan Semaan
While colonization has sociohistorically impacted people's identities across
various dimensions, those colonial values and biases continue to be perpetuated
by sociotechnical systems. One category of sociotechnical systems--sentiment
analysis tools--can also perpetuate colonial values and bias, yet less
attention has been paid to how such tools may be complicit in perpetuating
coloniality, although they are often used to guide various practices (e.g.,
content moderation). In this paper, we explore potential bias in sentiment
analysis tools in the context of Bengali communities that have experienced and
continue to experience the impacts of colonialism. Drawing on identity
categories most impacted by colonialism amongst local Bengali communities, we
focused our analytic attention on gender, religion, and nationality. We
conducted an algorithmic audit of all sentiment analysis tools for Bengali,
available on the Python package index (PyPI) and GitHub. Despite similar
semantic content and structure, our analyses showed that in addition to
inconsistencies in output from different tools, Bengali sentiment analysis
tools exhibit bias between different identity categories and respond
differently to different ways of identity expression. Connecting our findings
with colonially shaped sociocultural structures of Bengali communities, we
discuss the implications of downstream bias of sentiment analysis tools.