Moving beyond harm. A critical review of how NLP research approaches discrimination
Katrin Schulz, Marjolein Lanzing, Giulia Martinez Brenner

TL;DR
This paper critically reviews NLP research on discrimination, highlighting its focus on technological fixes and limited ethical vocabulary, and advocates shifting the focus to systemic injustice for more effective solutions.
Contribution
It offers a qualitative review of recent NLP discrimination research and proposes reframing the problem from harm to systemic injustice to improve understanding and solutions.
Findings
Research heavily focuses on algorithmic fixes.
Ethical vocabulary is limited to 'harm' and 'bias'.
Reframing to 'injustice' broadens systemic understanding.
Abstract
How to avoid discrimination in the context of NLP technology is one of the major challenges in the field. We propose that a different and more substantiated framing of the problem could help to find more productive approaches. In the first part of the paper we report on a case study: a qualitative review on papers published in the ACL anthologies 2022 on discriminatory behavior of NLP systems. We find that the field (i) still has a strong focus on a technological fix of algorithmic discrimination, and (ii) is struggling with a firm grounding of their ethical or normative vocabulary. Furthermore, this vocabulary is very limited, focusing mostly on the terms "harm" and "bias". In the second part of the paper we argue that addressing the latter problems might help with the former. The understanding of algorithmic discrimination as a technological problem is reflected in and reproduced by…
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