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

Prior research on exposure fairness in the context of recommender systems has
focused mostly on disparities in the exposure of individual or groups of items
to individual users of the system. The problem of how individual or groups of
items may be systemically under or over exposed to groups of users, or even all
users, has received relatively less attention. However, such systemic
disparities in information exposure can result in observable social harms, such
as withholding economic opportunities from historically marginalized groups
(allocative harm) or amplifying gendered and racialized stereotypes
(representational harm). Previously, Diaz et al. developed the expected
exposure metric -- that incorporates existing user browsing models that have
previously been developed for information retrieval -- to study fairness of
content exposure to individual users. We extend their proposed framework to
formalize a family of exposure fairness metrics that model the problem jointly
from the perspective of both the consumers and producers. Specifically, we
consider group attributes for both types of stakeholders to identify and
mitigate fairness concerns that go beyond individual users and items towards
more systemic biases in recommendation. Furthermore, we study and discuss the
relationships between the different exposure fairness dimensions proposed in
this paper, as well as demonstrate how stochastic ranking policies can be
optimized towards said fairness goals.

研究推荐系统中的曝光公平性，提出了一种从消费者和生产者双方角度建模的公平度量，以缓解除了个体用户和物品的不公平之外的更系统性的偏见，进一步研究了曝光公平性维度之间的关系并演示了如何将随机排序策略优化为这些公平目标。

面向多方曝光的推荐公平性

Joint Multisided Exposure Fairness for Recommendation

This special issue interrogates the meaning and impacts of "tech ethics": the
embedding of ethics into digital technology research, development, use, and
governance. In response to concerns about the social harms associated with
digital technologies, many individuals and institutions have articulated the
need for a greater emphasis on ethics in digital technology. Yet as more groups
embrace the concept of ethics, critical discourses have emerged questioning
whose ethics are being centered, whether "ethics" is the appropriate frame for
improving technology, and what it means to develop "ethical" technology in
practice. This interdisciplinary issue takes up these questions, interrogating
the relationships among ethics, technology, and society in action. This special
issue engages with the normative and contested notions of ethics itself, how
ethics has been integrated with technology across domains, and potential paths
forward to support more just and egalitarian technology. Rather than starting
from philosophical theories, the authors in this issue orient their articles
around the real-world discourses and impacts of tech ethics--i.e., tech ethics
in action.

该专刊探讨了将伦理学纳入数字技术研究、开发、使用和治理中的意义和影响，着眼于该领域内伦理、技术和社会之间的关系，旨在通过对实践中科技伦理的实际话语和影响进行探讨，支持更公正和平等的技术发展。