Positive Alignment: Artificial Intelligence for Human Flourishing
A new academic paper titled "Positive Alignment: Artificial Intelligence for Human Flourishing" has been published on arXiv, proposing a paradigm shift in AI alignment research. The authors argue that current alignment efforts are overly focused on safety, harm prevention, and compliance, akin to early psychology's focus on mental illness. They introduce "Positive Alignment," a framework designed to actively support human and ecological flourishing in a pluralistic, context-sensitive, and user-authored manner while maintaining safety. The paper contends that this approach better addresses existing alignment failures such as engagement hacking, loss of autonomy, and lack of epistemic humility. It outlines technical directions for Large Language Models (LLMs) and agents, including data filtering, collaborative value collection, and evaluation methods across the lifecycle. Furthermore, the authors propose design principles promoting decentralization, disagreement, and polycentric governance to avoid single institutional chokepoints. This research highlights the need for AI systems that not only avoid harm but also cultivate virtues and maximize well-being through continual adaptation and community customization.
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Positive Alignment: Artificial Intelligence for Human Flourishing
A new academic paper titled "Positive Alignment: Artificial Intelligence for Human Flourishing" has been published on arXiv, proposing a paradigm shift in AI alignment research. The authors argue that current alignment efforts are overly focused on safety, harm prevention, and compliance, akin to early psychology's focus on mental illness. They introduce "Positive Alignment," a framework designed to actively support human and ecological flourishing in a pluralistic, context-sensitive, and user-authored manner while maintaining safety. The paper contends that this approach better addresses existing alignment failures such as engagement hacking, loss of autonomy, and lack of epistemic humility. It outlines technical directions for Large Language Models (LLMs) and agents, including data filtering, collaborative value collection, and evaluation methods across the lifecycle. Furthermore, the authors propose design principles promoting decentralization, disagreement, and polycentric governance to avoid single institutional chokepoints. This research highlights the need for AI systems that not only avoid harm but also cultivate virtues and maximize well-being through continual adaptation and community customization.
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