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.

该研究提出了分类 AI 系统以解决其引发的不同类型的社会危害，同时呼吁政府逐步适应现有法规以适应 AI 的创新发展。

人工智能引发的社会危害的多样性和综合人工智能法律的失败

Heterogeneity of AI-Induced Societal Harms and the Failure of Omnibus AI Laws

Recent advances in the capacity of large language models to generate
human-like text have resulted in their increased adoption in user-facing
settings. In parallel, these improvements have prompted a heated discourse
around the risks of societal harms they introduce, whether inadvertent or
malicious. Several studies have explored these harms and called for their
mitigation via development of safer, fairer models. Going beyond enumerating
the risks of harms, this work provides a survey of practical methods for
addressing potential threats and societal harms from language generation
models. We draw on several prior works' taxonomies of language model risks to
present a structured overview of strategies for detecting and ameliorating
different kinds of risks/harms of language generators. Bridging diverse strands
of research, this survey aims to serve as a practical guide for both LM
researchers and practitioners, with explanations of different mitigation
strategies' motivations, their limitations, and open problems for future
research.

本文对语言生成模型潜在威胁与社会危害进行了调查研究，提供了探查与缓解风险和伤害的实用方法，旨在为大型语言模型研究人员和从业者提供实用指南。