Human-AI collaboration is considered the most promising way to incorporate AI in the workplace. What remains unexplored are the experiential consequences of this teaming. More specifically, in a team with AI, how humans perceive themselves (self-perception) and how they are perceived by their coworkers (peer perception) in terms of work ownership and job meaningfulness. In a 2x2x2 vignette study (n=50), participants rated perceptions of ownership, affect, job meaningfulness and satisfaction, and role dynamics across two levels (low/high) of AI proactivity and AI competency as within-subject factors, with point-of-view (self perception/peer perception) as between-subjects. Our results showed that AI with low competency or low proactivity generally improved feelings related to ownership, meaningfulness, satisfaction, and role dynamics, and also increased positive affect while reducing negative affect. However, these effects were often influenced by point-of-view. For instance, low AI proactivity resulted in higher job satisfaction from self-perception rather than peer perception. Based on our findings, we argue that designing AI for the future of work solely around performance metrics may not be adequate. Highly competent and proactive AI-driven systems can have undesirable impacts on perceptions of ownership, job identity, social image and team dynamics, and consequently, job meaningfulness.

翻译：人机协作被认为是将人工智能引入职场的最有前景方式。然而，这种协作带来的体验性后果仍未被充分探索。具体而言，在包含AI的团队中，人类如何在工作所有权和工作意义感方面认知自我（自我认知）以及被同事认知（同伴认知）。在一项2×2×2情境实验（n=50）中，参与者以被试内设计评估了AI主动性和AI能力的两个水平（高/低）对所有权感、情感、工作意义感与满意度以及角色动态的影响，同时以视角（自我认知/同伴认知）作为被试间变量。我们的结果显示，低能力或低主动性的AI普遍提升了与所有权、意义感、满意度和角色动态相关的感受，并增加了积极情感、减少了消极情感。然而，这些效应往往受到视角的影响。例如，低AI主动性通过自我认知而非同伴认知带来了更高的工作满意度。基于研究结果，我们认为仅围绕绩效指标设计面向未来工作的AI可能并不充分。高能力、高主动性的AI驱动系统可能对所有权感、工作认同、社会形象和团队动态产生不利影响，进而影响工作意义感。