伪造的前台：生成式AI与职场绩效的模糊性
The Fabricated Front: Generative AI and the Opacity of Workplace Performance
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中文总结 AI 辅助
该研究运用拟剧理论和Anthropic的AI面试官数据集，识别出五种职场模糊性机制，揭示专业人士维护身份机制却在劳动机制上制造模糊性的不对称，提出职场AI治理需明确可检查的人类参与形式。
中文摘要 AI 辅助
生成式AI（GenAI）已成为职场生活的固定组成部分。当前研究主要探讨这对以生产力、岗位替代或偏差衡量的工作及产出意味着什么，而GenAI在工作中产生的互动重构却未得到充分研究。新兴的努力模糊性概念已开始填补这一空白，其强调可观测产出与人类参与的系统性脱钩。当GenAI使互动线索的诊断性降低时，它会削弱维系协作信任的互惠交换。基于努力模糊性的这一解释，我们研究日常职场互动中产生模糊性的互动机制，运用欧文·戈夫曼的拟剧理论框架及Anthropic的AI面试官数据集里的1250份访谈记录，识别出五种重组职场前台的模糊性机制：语音（话语所指示的立场归属）、来源（谁能为成果背书）、脆弱性（员工是否不确定）、注意力（员工是否投入）、投入程度（成果反映了多少劳动）。我们发现，专业人士会维护身份机制，却在劳动机制周围随意制造模糊性，并将这种不对称追溯到当代以产出为中心的组织形式——交付物已取代了产生它们的劳动过程。因此，治理任务是参与管理：明确哪些形式的人类参与（注意力、努力、判断）必须保持可检查性，以及向谁开放。基于全面披露制定的职场AI政策将系统性地误判一个可检查性已相对受众的社会领域。
英文摘要
Generative AI (GenAI) has become a fixture of workplace life. Current research asks chiefly what this implies for jobs and outputs, measured in productivity, displacement, or bias. What remains underexamined are the interactional reconfigurations that GenAI produces at work. The emerging concept of effort opacity has begun to fill this gap by highlighting the systematic decoupling of observable output from human engagement. When GenAI makes interactional cues less diagnostic, it weakens the reciprocal exchange that sustains collaborative trust. Extending this account of effort opacity, we examine the interactional mechanics that produce opacity in everyday workplace encounters. Drawing on Erving Goffman's dramaturgical framework and 1,250 interview transcripts from Anthropic's AI Interviewer dataset, we identify five opacity mechanisms through which workplace fronts are reorganized: voice (whose stance the words index), provenance (who can stand behind the artifact), vulnerability (whether the worker is uncertain), attention (whether the worker is engaged), and investment (how much labor the output reflects). We show that professionals defend the identity mechanisms while freely producing opacity around the labor mechanisms, and trace this asymmetry to the output-centered organization of contemporary work, where deliverables already stand in for the labor process that produced them. The governance task, accordingly, is one of involvement management: specifying which forms of human involvement (attention, effort, judgment) must remain inspectable, and to whom. Workplace AI policies built on universal disclosure will systematically misrecognize a social field in which inspectability is already audience-relative.
