By defining the current limits (and thereby the frontiers), many boundaries
are shaping, and will continue to shape, the future of Artificial Intelligence
(AI). We push on these boundaries in order to make further progress into what
were yesterday's frontiers. They are both pliable and resilient - always
creating new boundaries of what AI can (or should) achieve. Among these are
technical boundaries (such as processing capacity), psychological boundaries
(such as human trust in AI systems), ethical boundaries (such as with AI
weapons), and conceptual boundaries (such as the AI people can imagine). It is
within this final category while it can play a fundamental role in all other
boundaries} that we find the construct of needs and the limitations that our
current concept of need places on the future AI.

该研究阐述了人工智能的现有限制和其未来的发展方向，人工智能的发展受技术、心理、伦理和概念等多种界限的制约，其中需求的概念和限制将对未来 AI 的发展产生重大影响。

面向需求的人工智能：服务于人类需求的 AI

Needs-aware Artificial Intelligence: AI that 'serves [human] needs'

Fairness in recommender systems has been considered with respect to sensitive
attributes of users (e.g., gender, race) or items (e.g., revenue in a
multistakeholder setting). Regardless, the concept has been commonly
interpreted as some form of equality -- i.e., the degree to which the system is
meeting the information needs of all its users in an equal sense. In this
paper, we argue that fairness in recommender systems does not necessarily imply
equality, but instead it should consider a distribution of resources based on
merits and needs.
We present a probabilistic framework based on generalized cross entropy to
evaluate fairness of recommender systems under this perspective, where we show
that the proposed framework is flexible and explanatory by allowing to
incorporate domain knowledge (through an ideal fair distribution) that can help
to understand which item or user aspects a recommendation algorithm is over- or
under-representing. Results on two real-world datasets show the merits of the
proposed evaluation framework both in terms of user and item fairness.

本文讨论推荐系统中的公平性，提出了一种基于概率的框架来评估推荐系统的公平性，该框架基于推荐算法偏向或低估哪些项目或用户方面的领域知识，并考虑到推荐资源如何基于优点和需求分配，研究结果表明所提出的评估框架在用户和项目公平性方面具有优点。