Recent research at the intersection of AI explainability and fairness has
focused on how explanations can improve human-plus-AI task performance as
assessed by fairness measures. We propose to characterize what constitutes an
explanation that is itself "fair" -- an explanation that does not adversely
impact specific populations. We formulate a novel evaluation method of "fair
explanations" using not just accuracy and label time, but also psychological
impact of explanations on different user groups across many metrics (mental
discomfort, stereotype activation, and perceived workload). We apply this
method in the context of content moderation of potential hate speech, and its
differential impact on Asian vs. non-Asian proxy moderators, across explanation
approaches (saliency map and counterfactual explanation). We find that saliency
maps generally perform better and show less evidence of disparate impact
(group) and individual unfairness than counterfactual explanations.
Content warning: This paper contains examples of hate speech and racially
discriminatory language. The authors do not support such content. Please
consider your risk of discomfort carefully before continuing reading!

最近在 AI 可解释性和公平性交叉领域的研究集中在解释如何提高人类加 AI 任务性能，以公平性指标评估。我们提出了对 “公平解释” 的特征进行表征，即不对特定人群产生负面影响的解释。我们提出了一种新颖的 “公平解释” 评估方法，不仅基于准确率和标签时间，还考虑了解释对不同用户群体的心理影响（心理不适，刻板印象激活和感知工作负荷）的多个指标。我们将该方法应用于潜在的仇恨言论的内容审查，以及其对亚洲和非亚洲代理审查员的差异影响，包括解释方法（显著性地图和反事实解释）。我们发现，显著性地图通常表现更好，显示出较少的不公平证据（群体和个体）比反事实解释。

迈向 “公正解释” 的概念化：针对内容审核员的反亚裔仇恨言论解释的不对称影响

Towards Conceptualization of "Fair Explanation": Disparate Impacts of  anti-Asian Hate Speech Explanations on Content Moderators

Besides far-reaching public health consequences, the COVID-19 pandemic had a
significant psychological impact on people around the world. To gain further
insight into this matter, we introduce the Real World Worry Waves Dataset
(RW3D). The dataset combines rich open-ended free-text responses with survey
data on emotions, significant life events, and psychological stressors in a
repeated-measures design in the UK over three years (2020: n=2441, 2021: n=1716
and 2022: n=1152). This paper provides background information on the data
collection procedure, the recorded variables, participants' demographics, and
higher-order psychological and text-based derived variables that emerged from
the data. The RW3D is a unique primary data resource that could inspire new
research questions on the psychological impact of the pandemic, especially
those that connect modalities (here: text data, psychological survey variables
and demographics) over time.

介绍了 Real World Worry Waves Dataset（RW3D）数据集，该数据集在英国三年内以重复测量设计结合了丰富的自由文本响应和关于情绪、重大生活事件和心理压力的调查数据（2020 年：n=2441，2021 年：n=1716 和 2022 年：n=1152），为未来研究提供了新的数据资源，探讨了 COVID-19 大流行对人们的心理影响。

RW3D：一个多模态数据集，用于了解疫情的心理影响

The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic

Augmented Reality or AR filters on selfies have become very popular on social
media platforms for a variety of applications, including marketing,
entertainment and aesthetics. Given the wide adoption of AR face filters and
the importance of faces in our social structures and relations, there is
increased interest by the scientific community to analyze the impact of such
filters from a psychological, artistic and sociological perspective. However,
there are few quantitative analyses in this area mainly due to a lack of
publicly available datasets of facial images with applied AR filters. The
proprietary, close nature of most social media platforms does not allow users,
scientists and practitioners to access the code and the details of the
available AR face filters. Scraping faces from these platforms to collect data
is ethically unacceptable and should, therefore, be avoided in research. In
this paper, we present OpenFilter, a flexible framework to apply AR filters
available in social media platforms on existing large collections of human
faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the
publicly available FairFace and LFW datasets and we outline insights derived
from the analysis of these beautified datasets.

本文介绍 OpenFilter, 一种灵活的框架可以应用社交媒体平台上的 AR 滤镜，以分析 AR 自拍滤镜的心理、艺术和社会学影响，同时分享了两个美化过的数据集 FairBeauty 和 B-LFW，并概述了从这些数据集分析得出的见解。