Despite widespread calls for transparent artificial intelligence systems, the
term is too overburdened with disparate meanings to express precise policy aims
or to orient concrete lines of research. Consequently, stakeholders often talk
past each other, with policymakers expressing vague demands and practitioners
devising solutions that may not address the underlying concerns. Part of why
this happens is that a clear ideal of AI transparency goes unsaid in this body
of work. We explicitly name such a north star -- transparency that is
user-centered, user-appropriate, and honest. We conduct a broad literature
survey, identifying many clusters of similar conceptions of transparency, tying
each back to our north star with analysis of how it furthers or hinders our
ideal AI transparency goals. We conclude with a discussion on common threads
across all the clusters, to provide clearer common language whereby
policymakers, stakeholders, and practitioners can communicate concrete demands
and deliver appropriate solutions. We hope for future work on AI transparency
that further advances confident, user-beneficial goals and provides clarity to
regulators and developers alike.

本文中，我们阐述了明确的 AI 透明度目标，即以用户为中心、恰当的透明度和诚实的透明度。我们进行了广泛的文献调查，将透明度概念的许多相似聚类与我们的目标联系起来，以便政策制定者、利益相关者和从业者能够交流具体需求和提供适当的解决方案。

用户是 AI 透明度的北极星

Users are the North Star for AI Transparency

AI plays a key role in current cyberspace and future immersive ecosystems
that pinpoint user experiences. Thus, the trustworthiness of such AI systems is
vital as failures in these systems can cause serious user harm. Although there
are related works on exploring trustworthy AI (TAI) metrics in the current
cyberspace, ecosystems towards user-centered services, such as the metaverse,
are much more complicated in terms of system performance and user experience
assessment, thus posing challenges for the applicability of existing
approaches. Thus, we give an overlook on fairness, privacy and robustness,
across the historical path from existing approaches. Eventually, we propose a
research agenda towards systematic yet user-centered TAI in immersive
ecosystems.

本研究探讨了现有方法对于以用户为中心的服务生态系统中 AI 信任度评估的适用性，并提出了一种面向系统化且以用户为中心的值得信赖的 AI 研究议程。

面向用户为中心的沉浸式虚拟环境可信人工智能度量

Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace

Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.

本文探讨广告的情感识别、编码方法与实际广告应用，从内容与用户两个视角出发，综合使用情感编码数据集、卷积神经网络、脑电图等技术手段，对相对情感编码方法进行对比研究，首次探究情感编码与广告实际效果的关系。