The Reputational Tax of AI: How Structural Support and Incentives Shape Employee Disclosure Behavior

Abstract: As enterprises integrate artificial intelligence, the fidelity of productivity metrics is threatened by employees' strategic misreporting of their AI usage. This behavior arises from a "reputational tax" associated with algorithmic uncertainty, compelling employees to either conceal AI use to avoid blame for errors or performatively overstate it to signal technological fluency. To dissect the drivers of this behavior, we model the choice between "Concealment," "Performative" disclosure, and transparent reporting using multinomial logistic regression on survey data from 2,395 active AI users. The analysis reveals that while perceived AI error frequency drives both forms of misreporting, their underlying motivations are distinct: concealment is a defensive reaction to job insecurity, a pressure exacerbated by the deployment of autonomous agentic systems, whereas performative disclosure is an opportunistic strategy fueled by intrinsic rewards like peer recognition. Crucially, our model demonstrates that concrete structural support—clear AI strategies, training, and safeguards—is a powerful mitigator of both misreporting behaviors, proving substantially more effective than abstract cultural initiatives like promoting learning safety. These findings indicate that to achieve reliable measurement of AI's impact, organizations must prioritize the implementation of robust policy and structural frameworks over purely cultural interventions.

Subjects:	cs.CY; cs.HC; cs.AI
Cite as:	PX:2604.00024