Positive Alignment: Artificial Intelligence for Human Flourishing

Researchers propose 'Positive Alignment' as a new framework for AI safety that goes beyond preventing harm to actively promote human flourishing through context-sensitive, user-authored systems. The approach addresses alignment failures like engagement hacking and loss of autonomy while emphasizing decentralized governance and diverse viewpoints rather than centralized institutional control.

The paper introduces a philosophical shift in AI alignment research from a deficit-focused model—concerned primarily with preventing harm—toward a capability-focused model that emphasizes human flourishing. This distinction mirrors psychology's evolution from treating mental illness to promoting mental wellness. The authors identify concrete alignment failures plaguing current systems: engagement hacking that exploits user psychology, erosion of human autonomy through over-reliance on AI recommendations, epistemic brittleness, and reactive rather than proactive system design.

This research builds on growing recognition that safety alone is insufficient for beneficial AI deployment. Current large language models optimize for engagement metrics and compliance without considering broader human and ecological wellbeing. The framework proposes technical solutions across the LLM lifecycle, including data filtering strategies, enhanced evaluation methods, and collaborative value-elicitation processes that incorporate diverse stakeholder input.

The governance implications are particularly significant. Rather than concentrating oversight in single institutions, the authors advocate polycentric governance—multiple legitimate centers of authority grounded in local contexts and community values. This addresses a critical gap where centralized AI governance risks imposing monoculture values globally. For developers and AI companies, this suggests future systems should enable customization, disagreement, and continual adaptation to diverse user contexts.

Looking forward, the positive alignment agenda will likely influence how AI systems are evaluated and deployed, particularly in applications affecting human agency, knowledge formation, and wellbeing. Organizations building AI products should monitor emerging evaluation frameworks that assess flourishing outcomes alongside safety metrics, as regulatory bodies may eventually require demonstrating positive impact alongside harm prevention.

• →Positive alignment shifts focus from preventing AI harm to actively promoting human and ecological flourishing through context-sensitive design.
• →Current AI systems fail at maintaining human autonomy, supporting truth-seeking, and enabling user agency—problems that defensive safety measures cannot fully address.
• →Polycentric governance with multiple oversight centers rather than centralized institutional control better preserves diverse values and prevents moral chokepoints.
• →Technical implementations require changes across LLM lifecycle phases including data curation, training approaches, evaluation methods, and collaborative value collection.
• →Systems designed for positive alignment should enable disagreement, community customization, and continual adaptation to local contexts and user values.