With the widespread adoption of advanced generative models such as Gemini and
GPT, there has been a notable increase in the incorporation of such models into
sociotechnical systems, categorized under AI-as-a-Service (AIaaS). Despite
their versatility across diverse sectors, concerns persist regarding
discriminatory tendencies within these models, particularly favoring selected
`majority' demographics across various sociodemographic dimensions. Despite
widespread calls for diversification of media representations, marginalized
racial and ethnic groups continue to face persistent distortion, stereotyping,
and neglect within the AIaaS context. In this work, we provide a critical
summary of the state of research in the context of social harms to lead the
conversation to focus on their implications. We also present open-ended
research questions, guided by our discussion, to help define future research
pathways.

在 AI-as-a-Service（AIaaS）背景下，我们对社会伤害的研究现状进行批判性总结，重点关注其影响，并提出开放性研究问题，以定义未来的研究方向。

从融合之地到曲解探索生成 AI 中的危害

From Melting Pots to Misrepresentations: Exploring Harms in Generative  AI

This paper details the privacy and security landscape in today's cloud
ecosystem and identifies that there is a gap in addressing the risks introduced
by machine learning models. As machine learning algorithms continue to evolve
and find applications across diverse domains, the need to categorize and
quantify privacy and security risks becomes increasingly critical. With the
emerging trend of AI-as-a-Service (AIaaS), machine learned AI models (or ML
models) are deployed on the cloud by model providers and used by model
consumers. We first survey the AIaaS landscape to document the various kinds of
liabilities that ML models, especially Deep Neural Networks pose and then
introduce a taxonomy to bridge this gap by holistically examining the risks
that creators and consumers of ML models are exposed to and their known
defences till date. Such a structured approach will be beneficial for ML model
providers to create robust solutions. Likewise, ML model consumers will find it
valuable to evaluate such solutions and understand the implications of their
engagement with such services. The proposed taxonomies provide a foundational
basis for solutions in private, secure and robust ML, paving the way for more
transparent and resilient AI systems.

云生态系统中的隐私和安全论文指出，解决机器学习模型引入的风险存在差距，通过调查 AIaaS 领域，提出了一种分类法来全面检查 ML 模型的创作者和消费者所面临的风险及其已知防御措施，为提供方和消费方提供了基础解决方案。