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.

翻译：随着人工智能（AI）的持续进步，确保其产生积极的社会影响变得至关重要，尤其是在AI系统日益渗透到生活各个方面的背景下。然而，开发"向善的AI"在使系统与复杂的人类价值观对齐方面提出了重大挑战。目前，我们缺乏成熟的方法来应对这些挑战。本文提出并评估了旨在填补这一空白的"积极AI"设计方法。该方法提供了一个以人为本的流程，将福祉愿景转化为具体实践。首先，我们阐释了该方法的四个关键步骤：情境化、操作化、优化和实施福祉，这些步骤由用于反馈循环的持续测量提供支持。随后，我们展示了一项多案例研究，其中新手设计师应用了该方法，揭示了其在有效性和可用性方面的优势与不足。接着，一项专家评估研究对产生的概念质量进行了评估，这些概念在可行性、可取性以及实现预期福祉效益的可能性方面获得了中等偏高的评分。综合来看，这些研究为该方​​法在改进AI设计方面的能力提供了初步验证，同时也揭示了需要完善的领域，例如为复杂步骤提供支持。提出的改进建议，如示例和评估启发式方法，可以解决现有不足。进一步的研究应考察该方法在多个项目中的持续应用效果。这种以人为本的方法为实现"促进福祉的AI"愿景带来了希望——这种AI不仅避免伤害，更能积极造福人类。