Monitoring wellbeing and stress is one of the problems covered by ambient
intelligence, as stress is a significant cause of human illnesses directly
affecting our emotional state. The primary aim was to propose a deliberation
architecture for an ambient intelligence healthcare application. The
architecture provides a plan for comforting stressed seniors suffering from
negative emotions in an assisted living home and executes the plan considering
the environment's dynamic nature. Literature was reviewed to identify the
convergence between deliberation and ambient intelligence and the latter's
latest healthcare trends. A deliberation function was designed to achieve
context-aware dynamic human-robot interaction, perception, planning
capabilities, reactivity, and context-awareness with regard to the environment.
A number of experimental case studies in a simulated assisted living home
scenario were conducted to demonstrate the approach's behavior and validity.
The proposed methods were validated to show classification accuracy. The
validation showed that the deliberation function has effectively achieved its
deliberative objectives.

提出了一个用于舒缓压力的老年人辅助居住环境下的反思架构，实现环境感知、规划能力和对环境的上下文感知，通过对实验案例研究进行验证，表明反思功能已有效实现其目标。

辅助居家生活的思辩感知环境智能系统

Deliberative Context-Aware Ambient Intelligence System for Assisted  Living Homes

Artificial Intelligence (AI) is transforming the world as we know it,
implying that it is up to the current generation to use the technology for
''good.'' We argue that making good use of AI constitutes aligning it with the
wellbeing of conscious creatures. However, designing wellbeing-aligned AI
systems is difficult. In this article, we investigate a total of twelve
challenges that can be categorized as related to a lack of knowledge (how to
contextualize, operationalize, optimize, and design AI for wellbeing), and lack
of motivation (designing AI for wellbeing is seen as risky and unrewarding).
Our discussion can be summarized into three key takeaways: 1) our understanding
of the impact of systems on wellbeing should be advanced, 2) systems should be
designed to promote and sustain wellbeing intentionally, and 3), above all,
Positive AI starts with believing that we can change the world for the better
and that it is profitable.

探讨如何设计人工智能系统以促进和保持个体幸福感，挑战包括对幸福感缺乏知识和对设计具有潜在风险和无动力。需要深入了解系统对幸福感的影响，刻意促进和保持幸福感，积极乐观地改变世界和获得利润。

正向人工智能：面向幸福导向的人工智能设计所面临的主要挑战

Positive AI: Key Challenges for Designing Wellbeing-aligned Artificial  Intelligence

Sensing technologies deployed in the workplace can collect detailed data
about individual activities and group interactions that are otherwise difficult
to capture. A hopeful application of these technologies is that they can help
businesses and workers optimize productivity and wellbeing. However, given the
inherent and structural power dynamics in the workplace, the prevalent approach
of accepting tacit compliance to monitor work activities rather than seeking
workers' meaningful consent raises privacy and ethical concerns. This paper
unpacks a range of challenges that workers face when consenting to workplace
wellbeing technologies. Using a hypothetical case to prompt reflection among
six multi-stakeholder focus groups involving 15 participants, we explored
participants' expectations and capacity to consent to workplace sensing
technologies. We sketched possible interventions that could better support more
meaningful consent to workplace wellbeing technologies by drawing on critical
computing and feminist scholarship -- which reframes consent from a purely
individual choice to a structural condition experienced at the individual level
that needs to be freely given, reversible, informed, enthusiastic, and specific
(FRIES). The focus groups revealed that workers are vulnerable to meaningless
consent -- dynamics that undo the value of data gathered in the name of
"wellbeing," as well as an erosion of autonomy in the workplace. To
meaningfully consent, participants wanted changes to how the technology works
and is being used, as well as to the policies and practices surrounding the
technology. Our mapping of what prevents workers from meaningfully consenting
to workplace wellbeing technologies (challenges) and what they require to do so
(interventions) underscores that the lack of meaningful consent is a structural
problem requiring socio-technical solutions.

本文使用假设案例引导了六个多利益相关者焦点小组，探索参与者对工作场所感知技术的期望和同意能力，描绘了可能更好地支持工作场所健康技术更有意义的同意的干预措施，强调缺乏有意义的同意是需要社会技术解决方案的结构性问题。