比较印美欧对算法绿色洗白的刑事追责,提出责任新框架
Algorithmic Criminal Liability in Greenwashing: Comparing India, United States, and European Union
- 基于判例与法律条文,分析算法生成虚假环保声明的责任归属
- 发现现有法律因强调人为意图而难以追责算法造假
- 建议建立结合算法风险评估的混合责任机制,适合政策制定者
人工智能驱动的绿色洗白已成为企业可持续治理中的隐蔽挑战,加剧了环境信息披露的不透明性并规避监管。本研究通过比较法分析印度、美国和欧盟在人工智能中介绿色洗白行为中的刑事法律责任,揭示了当误导性声明源自算法系统时归责机制的法律空白。现行法规普遍以人类主观故意为前提,难以应对算法误导。研究采用教义法学方法,系统剖析司法判例与立法工具,提出企业刑事责任可扩展至严格责任模式,需重构治理框架以实现算法问责,并在ESG体系下引入算法尽职调查义务。比较结果显示,欧盟企业可持续尽职调查指令(CSDDD)提供了潜在的跨国范式。本研究推动人工智能伦理与环境法发展,主张构建融合算法风险评估与法律人格概念的混合责任框架,确保算法透明度不足不成为免责理由。
原文摘要 · Abstract (English)
AI-powered greenwashing has emerged as an insidious challenge within corporate sustainability governance, exacerbating the opacity of environmental disclosures and subverting regulatory oversight. This study conducts a comparative legal analysis of criminal liability for AI-mediated greenwashing across India, the US, and the EU, exposing doctrinal lacunae in attributing culpability when deceptive claims originate from algorithmic systems. Existing statutes exhibit anthropocentric biases by predicating liability on demonstrable human intent, rendering them ill-equipped to address algorithmic deception. The research identifies a critical gap in jurisprudential adaptation, as prevailing fraud statutes remain antiquated vis-à-vis AI-generated misrepresentation. Utilising a doctrinal legal methodology, this study systematically dissects judicial precedents and statutory instruments, yielding results regarding the potential expansion of corporate criminal liability. Findings underscore the viability of strict liability models, recalibrated governance frameworks for AI accountability, and algorithmic due diligence mandates under ESG regimes. Comparative insights reveal jurisdictional disparities, with the EU Corporate Sustainability Due Diligence Directive (CSDDD) offering a potential transnational model. This study contributes to AI ethics and environmental jurisprudence by advocating for a hybrid liability framework integrating algorithmic risk assessment with legal personhood constructs, ensuring algorithmic opacity does not preclude liability enforcement.
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