As artificial intelligence (AI) continues advancing, ensuring positive
societal impacts becomes critical, especially as AI systems become increasingly
ubiquitous in various aspects of life. However, developing "AI for good" poses
substantial challenges around aligning systems with complex human values.
Presently, we lack mature methods for addressing these challenges. This article
presents and evaluates the Positive AI design method aimed at addressing this
gap. The method provides a human-centered process to translate wellbeing
aspirations into concrete practices. First, we explain the method's four key
steps: contextualizing, operationalizing, optimizing, and implementing
wellbeing supported by continuous measurement for feedback cycles. We then
present a multiple case study where novice designers applied the method,
revealing strengths and weaknesses related to efficacy and usability. Next, an
expert evaluation study assessed the quality of the resulting concepts, rating
them moderately high for feasibility, desirability, and plausibility of
achieving intended wellbeing benefits. Together, these studies provide
preliminary validation of the method's ability to improve AI design, while
surfacing areas needing refinement like developing support for complex steps.
Proposed adaptations such as examples and evaluation heuristics could address
weaknesses. Further research should examine sustained application over multiple
projects. This human-centered approach shows promise for realizing the vision
of 'AI for Wellbeing' that does not just avoid harm, but actively benefits
humanity.

人工智能的正向设计方法是通过追求幸福愿景、通过连续的反馈循环转化为具体实践的人性化过程，旨在改善人工智能设计，并通过案例研究和专家评估提供初步验证，表明其有望实现 'AI for Wellbeing' 的愿景。