Beyond Behaviorist Representational Harms: A Plan for Measurement and
  Mitigation
Jennifer Chien David Danks
Algorithmic harms are commonly categorized as either allocative or
representational. This study specifically addresses the latter, focusing on an
examination of current definitions of representational harms to discern what is
included and what is not. This analysis motivates our expansion beyond
behavioral definitions to encompass harms to cognitive and affective states.
The paper outlines high-level requirements for measurement: identifying the
necessary expertise to implement this approach and illustrating it through a
case study. Our work highlights the unique vulnerabilities of large language
models to perpetrating representational harms, particularly when these harms go
unmeasured and unmitigated. The work concludes by presenting proposed
mitigations and delineating when to employ them. The overarching aim of this
research is to establish a framework for broadening the definition of
representational harms and to translate insights from fairness research into
practical measurement and mitigation praxis.