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.

翻译：算法伤害通常被划分为分配性伤害和表征性伤害两类。本研究专门针对后者，通过审视当前表征性伤害的定义，辨析其涵盖与未涵盖的内容。这一分析促使我们突破行为主义定义的局限，扩展到对认知状态和情感状态伤害的考量。本文概述了测量的高阶要求：明确实施该方法所需专业知识，并通过案例研究加以阐释。我们的工作凸显了大语言模型在制造表征性伤害方面的独特脆弱性，尤其是在这些伤害未被测量和缓解的情况下。研究最后提出了缓解方案并界定了适用时机。本研究的总体目标是建立一个拓展表征性伤害定义的框架，并将公平性研究的见解转化为可操作的测量与缓解实践。