Positive Alignment: Artificial Intelligence for Human Flourishing

2026-05-11

AI Summary

Current AI alignment research predominantly emphasizes safety and harm prevention, often overlooking proactive objectives that foster human and ecological flourishing. This work proposes a novel “positive alignment” paradigm, advocating for a pluralistic, polycentric, context-sensitive, and user-driven approach that advances prosperity while ensuring safe collaboration. We systematically develop a full-lifecycle technical pathway encompassing data curation, pretraining and post-training optimization, collaborative value elicitation, and contextualized evaluation. The framework integrates mechanisms for cultivating AI virtues, supporting human autonomy, and enabling decentralized governance. It effectively addresses challenges such as engagement manipulation, insufficient epistemic humility, and value homogenization, offering design principles that accommodate value pluralism and community-specific customization. This approach provides a new direction for AI alignment that combines ethical depth with practical feasibility.

Abstract

Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way while (ii) remaining safe and cooperative. It is a distinct and necessary agenda within AI alignment research. We argue that several existing failures of alignment (e.g., engagement hacking, loss of human autonomy, failures in truth-seeking, low epistemic humility, error correction, lack of diverse viewpoints, and being primarily reactive rather than proactive) may be better addressed through positive alignment, including cultivating virtues and maximizing human flourishing. We highlight a range of challenges, open questions, and technical directions (e.g., data filtering and upsampling, pre- and post-training, evaluations, collaborative value collection) for different phases of the LLM and agents lifecycle. We end with design principles for promoting disagreement and decentralization through contextual grounding, community customization, continual adaptation, and polycentric governance; that is, many legitimate centers of oversight rather than one institutional or moral chokepoint.

Problem

• AI alignment
• human flourishing
• positive alignment
• autonomy
• epistemic humility

Innovation

• Positive Alignment
• Human Flourishing
• Polycentric Governance
• Contextual Grounding
• Virtue Cultivation