AI-induced societal harms mirror existing problems in domains where AI replaces or complements traditional methodologies. However, trustworthy AI discourses postulate the homogeneity of AI, aim to derive common causes regarding the harms they generate, and demand uniform human interventions. Such AI monism has spurred legislation for omnibus AI laws requiring any high-risk AI systems to comply with a full, uniform package of rules on fairness, transparency, accountability, human oversight, accuracy, robustness, and security, as demonstrated by the EU AI Regulation and the U.S. draft Algorithmic Accountability Act. However, it is irrational to require high-risk or critical AIs to comply with all the safety, fairness, accountability, and privacy regulations when it is possible to separate AIs entailing safety risks, biases, infringements, and privacy problems. Legislators should gradually adapt existing regulations by categorizing AI systems according to the types of societal harms they induce. Accordingly, this paper proposes the following categorizations, subject to ongoing empirical reassessments. First, regarding intelligent agents, safety regulations must be adapted to address incremental accident risks arising from autonomous behavior. Second, regarding discriminative models, law must focus on the mitigation of allocative harms and the disclosure of marginal effects of immutable features. Third, for generative models, law should optimize developer liability for data mining and content generation, balancing potential social harms arising from infringing content and the negative impact of excessive filtering and identify cases where its non-human identity should be disclosed. Lastly, for cognitive models, data protection law should be adapted to effectively address privacy, surveillance, and security problems and facilitate governance built on public-private partnerships.

翻译：AI引发的社会危害反映了AI替代或补充传统方法的领域中存在的既有问题。然而，可信AI论述假设AI具有同质性，试图推导其产生危害的共同原因，并要求采用统一的人类干预措施。这种AI一元论推动了综合AI法律的立法，要求所有高风险AI系统遵守一整套关于公平性、透明度、问责制、人类监督、准确性、鲁棒性和安全性的统一规则，欧盟AI法规和美国《算法问责法案》草案即为明证。然而，当有可能将涉及安全风险、偏见、侵权和隐私问题的AI区分开来时，要求高风险或关键AI遵守所有安全、公平、问责和隐私法规是不合理的。立法者应根据AI引发的社会危害类型对AI系统进行分类，逐步调整现有法规。据此，本文提出以下分类，并需持续进行实证重新评估。首先，对于智能体，安全法规必须加以调整以应对自主行为引发的渐增事故风险。其次，对于判别模型，法律应侧重于减轻分配性危害并披露不可变特征的边际效应。第三，对于生成模型，法律应优化开发者在数据挖掘和内容生成方面的责任，平衡侵权内容可能造成的社会危害与过度过滤的负面影响，并明确其非人类身份应予以披露的情形。最后，对于认知模型，数据保护法应加以调整以有效应对隐私、监控和安全问题，并促进基于公私合作伙伴关系的治理架构。