On Defining Erasure Harms for NLP
Yu Lu Liu, Arnav Goel, Jackie Chi Kit Cheung, Alexandra Olteanu, Ziang Xiao, Su Lin Blodgett
Abstract
The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has begun to conceptualize and measure one such harm, the harm of erasure. Nevertheless, the field lacks a clear and cohesive conceptual foundation for identifying and measuring erasure. Existing conceptualizations of erasure are often broad -- making it difficult to identify what is needed to establish and measure erasure -- or else specific to particular settings -- facilitating measurement for those settings but potentially challenging to adapt to other settings. To address this gap, we develop and propose a structured definition of erasure that clarifies what components are necessary for establishing whether erasure has occurred, which practitioners need to explicitly articulate and operationalize in order to measure erasure.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.