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，确保其对社会的积极影响变得至关重要，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"向善的人工智能"面临着将系统与复杂的人类价值观对齐的重大挑战。目前，我们缺乏成熟的方法来应对这些挑战。本文介绍并评估了旨在填补这一空白的积极人工智能设计方法。该方法提供了一个以人为中心的过程，将福祉愿望转化为具体实践。首先，我们阐述了该方法的四个关键步骤：情境化、操作化、优化和实施福祉，并通过持续测量形成反馈循环。随后，我们进行了一项多案例研究，由新手设计师应用该方法，揭示了其有效性和可用性方面的优势与不足。接着，一项专家评估研究评价了由此产生的概念质量，在可行性、可取性以及实现预期福祉效益的合理性方面给予了中等偏高的评分。这些研究共同为该方法提升AI设计的能力提供了初步验证，同时也揭示了需要改进的领域，例如为复杂步骤提供支持。提出的改进措施，如示例和评估启发式方法，可以弥补这些不足。未来研究应考察其在多个项目中的持续应用。这种以人为本的方法展现出实现"福祉人工智能"愿景的潜力，不仅避免造成伤害，更能主动造福人类。