Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening

Title:Whose Voice Survives the Summary? A Voice-Retention Audit of LLM Employee Listening

Abstract:Organizations increasingly route employee feedback to leaders through large language model (LLM) summaries, an unaudited layer that silences already-spoken voice. We introduce a Voice Retention / Representation Ratio metric for representational bias in summarization and apply it to a bilingual (English/German) corpus of 2,586 free-text responses from a global professional service company. First, employees supply criticism more reliably than praise (withholding praise is 82 times more common). Second, across 45 leader-summaries the pipeline filters by popularity, not sentiment: criticism survives, yet a concern voiced once is dropped 86% of the time, with short and German-only content lost on the same axis (theme retention 0.14 vs 0.74; German directional). Controlling for frequency, sentiment has no independent effect; the harm is prevalence-driven, which sentiment-only audits miss. A targeted prompt recovers only named themes. We contribute the metric, field evidence, and a disaggregated voice-retention card.

10 pages, 3 figures, 3 tables. Accepted at the 60th Hawaii International Conference on System Sciences (HICSS 2027)

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)

arXiv:2609.38818 [cs.AI]

(or arXiv:2609.38818v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.38818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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