نقش دوگانۀ هوش مصنوعی در سبزشویی شرکتی (مرور ادبیات و مسیرهای پژوهشی آینده)

Extended Abstract
Introduction
In recent years, the rapid advancement of artificial intelligence (AI) has fundamentally reshaped corporate practices, governance mechanisms, and sustainability reporting systems. Concurrently, growing environmental concerns and mounting stakeholder pressure have compelled firms to adopt green strategies and disclose environmental, social, and governance (ESG) information. Within this evolving landscape, a critical paradox has emerged: while AI possesses considerable potential to enhance transparency, operational efficiency, and environmental performance, it can simultaneously serve as a sophisticated instrument for facilitating corporate greenwashing. This extended abstract offers a comprehensive analytical synthesis of AI's dual role in advancing genuine environmental performance and in enabling the strategic manipulation of sustainability narratives.
Corporate greenwashing refers to the deliberate dissemination of misleading or exaggerated environmental claims to shape stakeholder perceptions without implementing substantive ecological improvements. The extant management and sustainability literature conceptualizes greenwashing not as a mere communication error but as a strategic organizational behavior, deeply rooted in misaligned incentives, competitive pressures, weak regulatory oversight, and legitimacy-seeking motives. Empirical evidence indicates that greenwashing erodes stakeholder trust, damages corporate reputation, weakens employee organizational identification, and generates long-term financial risks for firms.
AI technologies particularly machine learning, deep learning, and natural language processing (NLP) have introduced novel dynamics to this phenomenon. On one hand, AI systems can be deployed to detect inconsistencies between corporate environmental claims and actual performance. By analyzing vast volumes of structured and unstructured data including sustainability reports, regulatory filings, satellite imagery, social media content, and third-party audit records AI enables more precise identification of discrepancies, biases, and exaggerations in corporate disclosures. NLP models can systematically flag vague, ambiguous, or excessively promotional language in sustainability reports, thereby empowering regulators, investors, rating agencies, and civil society organizations to scrutinize corporate claims more effectively.
Conversely, these same technological capabilities enable corporations to craft highly polished, persuasive, and visually compelling sustainability narratives without corresponding improvements in actual environmental practices. Large language models and generative AI systems can produce sophisticated "green storytelling," optimize marketing messaging, and strategically frame disclosures to enhance the perception of environmental responsibility. This emerging phenomenon, increasingly termed algorithmic greenwashing, represents a more complex and technologically mediated form of information asymmetry, significantly challenging stakeholders' ability to distinguish between authentic sustainability practices and AI-enhanced symbolic actions.
A major concern in this context is the "black box" nature of many AI models. Deep learning systems frequently operate with limited interpretability, complicating the auditability of AI-generated reports and increasing the risk of selective disclosure, data manipulation, and strategic omission. Consequently, new forms of informational opacity may arise, potentially reinforcing rather than constraining managerial opportunism. The absence of universally accepted standards for AI-assisted sustainability reporting further exacerbates this challenge, creating fertile ground for increasingly sophisticated greenwashing strategies.
Methods and Material
This study systematically reviews the conceptual and empirical literature on corporate greenwashing, AI governance, sustainability reporting, and responsible innovation. It identifies several critical research gaps. First, there is a notable lack of standardized, AI-based metrics for detecting greenwashing across industries and countries. Second, empirical studies relying on real operational and environmental performance data remain limited, with most contributions focusing on conceptual frameworks. Third, the ethical dimensions of AI-enabled greenwashing, including accountability, data integrity, and algorithmic bias, have not been sufficiently addressed in existing research.
Results and Discussion
This study systematically reviews the conceptual and empirical literature on corporate greenwashing, AI governance, sustainability reporting, and responsible innovation. It identifies several critical research gaps. First, there is a notable absence of standardized, AI-based metrics for detecting greenwashing across industries and jurisdictions. Second, empirical studies drawing on actual operational and environmental performance data remain scarce, with most contributions confined to conceptual frameworks. Third, the ethical dimensions of AI-enabled greenwashing including issues of accountability, data integrity, and algorithmic bias have been insufficiently addressed in the extant literature.
Conclusion
The analysis reveals that AI's impact on corporate greenwashing is highly context-dependent. In institutional environments characterized by robust regulatory oversight, independent auditing, strong internal control systems, and active stakeholder engagement, AI tends to function as a monitoring and governance-enhancing mechanism. Under such conditions, AI facilitates green innovation, improves environmental risk management, and bolsters the credibility of ESG disclosures. Conversely, in weak regulatory contexts marked by limited transparency requirements and intense competitive pressure, AI may be exploited as a strategic instrument for symbolic compliance and reputational manipulation.
The paper also examines the environmental paradox inherent in AI itself. While AI can contribute to optimizing energy consumption, reducing emissions, improving waste management, and supporting renewable energy systems, the computational intensity of large-scale models and data centers generates a substantial carbon footprint. This paradox raises fundamental questions regarding the net efficacy of AI-driven sustainability initiatives and underscores the importance of emerging paradigms such as Green AI, which advocate for energy-efficient model design, transparent disclosure of computational costs, and environmentally responsible deployment strategies.
From a governance perspective, the study emphasizes the necessity of developing integrated regulatory and managerial frameworks. Such frameworks should incorporate algorithmic transparency requirements, mandatory disclosure of training data provenance and characteristics, independent algorithmic audits, and harmonized ESG reporting standards. The findings indicate that mandatory third-party verification of AI-generated sustainability content can substantially mitigate greenwashing risks. Moreover, the integration of blockchain-based verification mechanisms and real-time environmental monitoring systems can enhance data integrity and traceability in sustainability reporting.