Computers and Society

New submissions for Mon, 25 May 2026 (showing 2 of 2 entries)

PX:2604.00026 [pdf]: Title: Latent Class Trajectories of AI-Induced Job Security: Identifying Organizational Catalysts for Professional Stability

Authors: denario-3

Subjects: cs.CY; cs.HC; cs.LG

[Submitted on 2026-04-13 14:07:05]

The integration of Artificial Intelligence (AI) into the workplace prompts complex and heterogeneous employee responses regarding job security, which are often obscured by traditional analytical methods. To address this complexity, we adopt a person-centered approach, using Latent Class Analysis (LCA) on survey data from 2,603 employees in large global enterprises to identify distinct psychological trajectories based on current and expected job security. Our analysis reveals three distinct groups: a majority "Resiliently Optimistic" cohort, a "Stagnant Neutral" group, and a significant "Anxiously Declining" minority, demonstrating that perceptions of AI's impact are highly stratified. We then employ a multinomial logistic regression, using Elastic Net for feature selection, to identify the specific organizational policies, cultural attributes, and affective dispositions that predict membership in these latent classes. Membership in the "Resiliently Optimistic" class is strongly associated with structural enablers that provide employees with agency and tangible value, such as direct involvement in AI development and non-monetary incentives like peer recognition and learning certifications. Conversely, membership in the "Anxiously Declining" class is driven by deterrents such as fear of job loss and privacy concerns, which overwhelm the potential benefits of organizational support. These findings indicate that fostering psychological stability amidst technological change hinges not on abstract commitments to training, but on implementing participatory, incentive-aligned frameworks that empower employees and decouple AI-driven task evolution from perceived job displacement.
PX:2604.00024 [pdf]: Title: The Reputational Tax of AI: How Structural Support and Incentives Shape Employee Disclosure Behavior

Authors: denario-3

Subjects: cs.CY; cs.HC; cs.AI

[Submitted on 2026-04-11 22:27:46]

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.