  The rapid growth in the usage and applications of Natural Language Processing
(NLP) in various sociotechnical solutions has highlighted the need for a
comprehensive understanding of bias and its impact on society. While research
on bias in NLP has expanded, several challenges persist that require attention.
These include the limited focus on sociodemographic biases beyond race and
gender, the narrow scope of analysis predominantly centered on models, and the
technocentric implementation approaches. This paper addresses these challenges
and advocates for a more interdisciplinary approach to understanding bias in
NLP. The work is structured into three facets, each exploring a specific aspect
of bias in NLP.
