The rapid evolution of artificial intelligence (AI) systems, tools, and technologies has opened up novel, unprecedented opportunities for businesses to innovate, differentiate, and compete. However, growing concerns have emerged about the use of AI in businesses, particularly AI washing, in which firms exaggerate, misrepresent, or superficially signal their AI capabilities to gain financial and reputational advantages. This paper aims to establish a conceptual foundation for understanding AI washing. In this paper, we draw on analogies from greenwashing and insights from Information Systems (IS) research on ethics, trust, signaling, and digital innovation. This paper proposes a typology of AI washing practices across four primary domains: marketing and branding, technical capability inflation, strategic signaling, and governance-based washing. In addition, we examine their organizational, industry, and societal impacts. Our investigation and analysis reveal how AI washing can lead to short-term gains; however, it also proposes severe long-term consequences, including reputational damage, erosion of trust, and misallocation of resources. Moreover, this paper examines current research directions and open questions aimed at mitigating AI washing practices and enhancing the trust and reliability of legitimate AI systems and technologies.

翻译：人工智能（AI）系统、工具和技术的快速发展为企业创新、差异化与竞争带来了前所未有的新机遇。然而，人们对企业在商业中使用人工智能的担忧日益增长，尤其是“AI 清洗”现象——即企业夸大、歪曲或表面化地标榜其 AI 能力，以获取财务和声誉上的优势。本文旨在为理解 AI 清洗建立一个概念基础。我们借鉴了“绿色清洗”的类比，并融合了信息系统（IS）研究中关于伦理、信任、信号传递与数字创新的见解。本文提出了一个涵盖四个主要领域的 AI 清洗实践类型学：市场营销与品牌塑造、技术能力夸大、战略性信号传递以及基于治理的清洗。此外，我们还探讨了这些实践对组织、行业和社会的影响。我们的调查与分析揭示了 AI 清洗如何可能带来短期收益，但也指出了其可能导致严重的长期后果，包括声誉损害、信任侵蚀以及资源错配。最后，本文审视了当前旨在缓解 AI 清洗实践、增强合法 AI 系统与技术可信度与可靠性的研究方向及开放性问题。