MathGloss is a project to create a knowledge graph (KG) for undergraduate
mathematics from text, automatically, using modern natural language processing
(NLP) tools and resources already available on the web. MathGloss is a linked
database of undergraduate concepts in mathematics. So far, it combines five
resources: (i) Wikidata, a collaboratively edited, multilingual knowledge graph
hosted by the Wikimedia Foundation, (ii) terms covered in mathematics courses
at the University of Chicago, (iii) the syllabus of the French undergraduate
mathematics curriculum which includes hyperlinks to the automated theorem
prover Lean 4, (iv) MuLiMa, a multilingual dictionary of mathematics curated by
mathematicians, and (v) the nLab, a wiki for category theory also curated by
mathematicians. MathGloss's goal is to bring together resources for learning
mathematics and to allow every mathematician to tailor their learning to their
own preferences. Moreover, by organizing different resources for learning
undergraduate mathematics alongside those for learning formal mathematics, we
hope to make it easier for mathematicians and formal tools (theorem provers,
computer algebra systems, etc) experts to "understand" each other and break
down some of the barriers to formal math.

MathGloss 项目旨在使用现有的现代自然语言处理（NLP）工具和网络资源，自动创建一个用于本科数学的知识图谱（KG）。MathGloss 是一个链接数据库，已整合五个资源，目标是为数学学习资源进行整合，并允许每个数学家根据自己的偏好进行学习。此外，将不同的本科数学学习资源与学习正式数学的资源组织在一起，希望能够促进数学家和正式工具专家（定理证明器、计算代数系统等）之间的相互理解，并消除一些形式数学方面的障碍。

MathGloss: 从文本构建数学术语表

MathGloss: Building mathematical glossaries from text

Fairlearn is an open source project to help practitioners assess and improve
fairness of artificial intelligence (AI) systems. The associated Python
library, also named fairlearn, supports evaluation of a model's output across
affected populations and includes several algorithms for mitigating fairness
issues. Grounded in the understanding that fairness is a sociotechnical
challenge, the project integrates learning resources that aid practitioners in
considering a system's broader societal context.

Fairlearn 是一个开源项目，旨在帮助从业人员评估和提高人工智能系统的公平性。项目包括名为 fairlearn 的 Python 库，支持跨受影响人群评估模型输出，并包含多种解决公平性问题的算法。在公平性是一个社会技术挑战的认识基础上，该项目整合学习资源，帮助从业人员考虑系统在更广泛的社会背景下的作用。