While several previous studies have analyzed gender bias in research, we are
still missing a comprehensive analysis of gender differences in the AI
community, covering diverse topics and different development trends. Using the
AI Scholar dataset of 78K researchers in the field of AI, we identify several
gender differences: (1) Although female researchers tend to have fewer overall
citations than males, this citation difference does not hold for all
academic-age groups; (2) There exist large gender homophily in co-authorship on
AI papers; (3) Female first-authored papers show distinct linguistic styles,
such as longer text, more positive emotion words, and more catchy titles than
male first-authored papers. Our analysis provides a window into the current
demographic trends in our AI community, and encourages more gender equality and
diversity in the future. Our code and data are at
this https URL

本研究通过对 AI Scholar 数据集的分析，发现 AI 领域存在性别差异，包括论文引用、合著者中性别同质性和女性第一作者论文具有更长的文本长度、更多积极情感词和更容易引起读者注意的标题。这些发现有助于促进 AI 领域的性别平等和多样性。

分析 AI 出版世界中性别差异

Voices of Her: Analyzing Gender Differences in the AI Publication World

Motivated by mitigating potentially harmful impacts of technologies, the AI
community has formulated and accepted mathematical definitions for certain
pillars of accountability: e.g. privacy, fairness, and model transparency. Yet,
we argue this is fundamentally misguided because these definitions are
imperfect, siloed constructions of the human values they hope to proxy, while
giving the guise that those values are sufficiently embedded in our
technologies. Under popularized methods, tensions arise when practitioners
attempt to achieve each pillar of fairness, privacy, and transparency in
isolation or simultaneously. In this position paper, we push for redirection.
We argue that the AI community needs to consider all the consequences of
choosing certain formulations of these pillars -- not just the technical
incompatibilities, but also the effects within the context of deployment. We
point towards sociotechnical research for frameworks for the latter, but push
for broader efforts into implementing these in practice.

我们认为社区旨在减轻技术的潜在危害影响，通过定义公私保密、公平和透明模型等一些责任支柱作为指标是根本错误的，因为这些定义是人类价值的不完美、独立构建，而带着那些价值完全嵌入我们的技术的幌子。我们推动 AI 社区重新考虑某些支柱公式的选择带来的所有后果，而不仅仅是技术上的不兼容性，而是在部署背景下的影响。我们指出社会技术研究可以为后者提供框架，但还应推动更广泛的实践落地。

人工智能中人类价值的代理机构之间的紧张关系

Tensions Between the Proxies of Human Values in AI

Big data have the characteristics of enormous volume, high velocity,
diversity, value-sparsity, and uncertainty, which lead the knowledge learning
from them full of challenges. With the emergence of crowdsourcing, versatile
information can be obtained on-demand so that the wisdom of crowds is easily
involved to facilitate the knowledge learning process. During the past thirteen
years, researchers in the AI community made great efforts to remove the
obstacles in the field of learning from crowds. This concentrated survey paper
comprehensively reviews the technical progress in crowdsourcing learning from a
systematic perspective that includes three dimensions of data, models, and
learning processes. In addition to reviewing existing important work, the paper
places a particular emphasis on providing some promising blueprints on each
dimension as well as discussing the lessons learned from our past research
work, which will light up the way for new researchers and encourage them to
pursue new contributions.

本文从数据、模型、学习三个维度，全面回顾了 13 年来 AI 社区在众包学习领域的进展，着重提出了每个维度的一些有前途的蓝图，并探讨了过去研究的经验教训，旨在为新研究者提供指引，鼓励他们做出新的贡献。

众包知识学习：简要综述与系统视角

Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective

In response to a national and international awakening on the issues of
anti-Blackness and systemic discrimination, we have penned this piece to serve
as a resource for allies in the AI community who are wondering how they can
more effectively engage with dismantling racist systems. This work aims to help
elucidate areas where the AI community actively and passively contributes to
anti-Blackness and offers actionable items on ways to reduce harm.

为 AI 界的盟友们提供资源，帮助他们理解 AI 界积极和消极参与反黑人主义的方面，并提供可行的措施以减少伤害。