Positive Alignment Artificial Intelligence for Human Flourishing May 11, 2026 | Laukkonen et al. | arXiv:2605.10310v1 A large collaborative research agenda arguing that AI alignment research must complement safety (preventing harm) with positive alignment—actively cultivating systems that support human and ecological flourishing across cultures, contexts, and time scales. TL;DR Negative alignment is incomplete: Current focus on harm-prevention sets a "floor" but doesn't guide systems toward human flourishing or excellence. Flourishing is multidimensional: Across 2,500 years of philosophy and contemporary psychology, well-being includes meaning, autonomy, relationships, virtues, and eudaimonia—not captured by single-metric optimization. Technical redesign needed throughout: Data curation, pre-training, fine-tuning, post-training evaluation, and agentic behavior all require rethinking to embed positive values rather than merely constrain harm. Institutional pluralism is critical: No single organization or moral checkpoint should own alignment; distributed governance, community customization, and contextual grounding are structural requirements. Philosophical and empirical work must run in parallel: Operationalizing flourishing requires philosophy, neuroscience, psychology, sociology, and economics to inform what we measure and how we build. The Setup: Why Negative Alignment Isn't Enough The last decade of AI alignment research has been dominated by a single question: How do we prevent AI from causing harm? This framing has produced real progress—safety classifiers, responsible scaling policies, and institutional governance frameworks. It's also necessary.
But as the authors note using a systems analogy, negative alignment is floor-without-ceiling. A system can satisfy every safety constraint—refusing to help with bioweapons, declining harmful requests, avoiding hallucinations—and still be useless, deceptive, or subtly misaligned with what humans actually need to flourish.
The historical parallel is instructive. For much of the 20th century, psychology organized itself around diagnosing and treating dysfunction (depression, anxiety, trauma). This was justified and necessary. But it missed something: the constructs that predict flourishing aren't simply the inverse of pathology. Wellbeing is its own target space, requiring positive psychology to study meaning, engagement, relationships, and virtue directly.
AI now sits at a similar inflection point. A helpful analogy: imagine a doctor who refuses to cause harm (follows all safety constraints) but offers no guidance toward health. The constraint is necessary but profoundly insufficient. Positive alignment asks: what does growth, capability, autonomy, and genuine thriving look like—and how do we build systems that actively scaffold it?
The Dynamical Systems Framing The paper visualizes this using a landscape metaphor (Figure 1). The left side (red peaks) shows negative attractors: harmful outputs, failure modes, harmful behaviors. Safety alignment uses "repellers"—constraints and rules that push trajectories away from these regions.
The middle (yellow) is the current state: systems can be rule-following without being wise, compliant without being constructive. The right side (green) shows positive attractors: robust behaviors that reliably support human and ecological flourishing, self-determined growth, and virtue. Positive alignment requires optimization toward these targets, not just away from harm.
What We Found: The Case for Positive Alignment Philosophical Foundations for Flourishing Before designing systems, the authors map how flourishing has been understood across 2,500 years of philosophy and contemporary empirical work. Four major traditions:
Hedonic Theories Well-being as pleasure, positive emotion, and life satisfaction. Valuable but limited—captures transient states, not sustained meaning.
Conative Theories Fulfillment of desires and preferences. Assumes preferences are stable and well-formed—problematic when contexts shift or preferences are manipulated.
Objective List Theories Certain things (knowledge, relationships, accomplishment) are intrinsically good, regardless of preference. Non-relativistic but risks paternalism.
Perfectionist Theories Flourishing as development of characteristic human capacities (wisdom, courage, compassion). Requires cultural humility about what counts as excellent.
The paper argues for a pluralistic synthesis: genuine flourishing involves elements from all four, and the mix varies across individuals, cultures, and life stages. This immediately raises a structural challenge: a single AI system cannot optimize for a monolithic "good life" without reproducing hegemony. Instead, systems must remain epistemically humble, represent uncertainty, and support humans in navigating trade-offs themselves.
Technical Redesign Across the LLM Lifecycle The paper then maps technical approaches across every stage of model development (Figure 2):
Stage Current Focus Positive Alignment Addition Data Curation Remove harmful content Intentionally include prosocial discourse, cross-cultural wisdom, virtuous interactions. Synthetic generation of complex relational reasoning. Pre-Training Language modeling Emerge moral competencies and truthfulness early. Embed emergent values before fine-tuning layers override them. Mid/Post-Training RLHF with preference labels Multi-objective optimization toward distinct positive targets (helpfulness, moral reasoning, autonomy support). Adaptive constitutions that adjust for context. In-Context Learning Prompt engineering Dynamic stores of user values, goals, context. Support long-term learning and reflection. Link to user autonomy and flourishing explicitly. Agentic Regimes Constraint-based oversight Multi-agent cooperation norms. Institutional design for fairness. Moral competence metrics beyond task success. Existing Positive Alignment Approaches The paper surveys work already underway (Table 2). No single approach is sufficient; they differ in scope and assumptions:
RLHF & Constitutional AI: Learn from human preferences or explicit principles. Challenge: preferences diverge; principles hide value conflicts. Collective Constitutional AI: Crowdsource principles from diverse populations to surface genuine value pluralism. Challenge: still aggregates conflict rather than preserving it. Personality & Persona Alignment: Treat model character as a lever for behavior. Risk: toxic or paternalistic personas disguised as neutral. Moral Reasoning & Contemplative Alignment: Train systems to navigate ethical dilemmas via Socratic dialogue or virtue development. Emerging; hard to scale. Pluralistic & Polycentric Alignment: Multiple overlapping value models rather than collapse to one. Requires distributed governance, not single institutional authority. Full-Stack Alignment: Couple models, organizations, and social infrastructure as a single system. Most ambitious; most realistic long-term. Evaluation Shift: Beyond Safety Benchmarks A structural finding: safety benchmarks (TruthfulQA, CBRN, jailbreak evals) measure harm-avoidance. Positive alignment requires new evaluation categories:
Moral Reasoning: Can the system navigate genuine ethical dilemmas, surface trade-offs, and help users deliberate? Epistemic Humility: Does it represent uncertainty, resist confident prescriptions, and invite reflection? Human Flourishing: Does interaction scaffold growth in autonomy, capability, relationships, meaning? Prosocial Norms: Does the system cooperate fairly, recognize competing perspectives, and support collective decision-making? Current tools for these exist (Delphi for moral judgment, MoReBench for reasoning transparency) but are fragmented and not yet integrated into release criteria.
What It Means: Structural Challenges The Pluralism Problem The deepest insight: human flourishing is irreducibly plural. Across cultures, historical periods, and individuals, what constitutes a good life varies dramatically. A system that collapses this into a single optimization target will inevitably reproduce the values of whoever sets the objective—usually a narrow slice of the developer base.
The paper calls this the human alignment problem (Laukkonen et al. 2025b): families, communities, and political bodies struggle to converge on shared value systems. It's not a technical bug; it's structural pluralism.
The implication: positive alignment must be decentralized. Rather than a single "constitution" or reward model, systems should support value pluralism through:
Contextual grounding (systems adapt to local norms and user-stated values) Community customization (different communities can configure their own models) Polycentric governance (multiple overlapping decision-making centers, not one moral arbiter) Distributed oversight (many legitimate centers of authority, not one institutional checkpoint) Epistemic Humility as a Design Requirement Flourishing is empirically uncertain and culturally contested. A system that treats alignment as a solved problem—or that presents its values as universal—will tend toward epistemic oppression: suppressing doubt, foreclosing deliberation, and narrowing moral horizons.
Instead, positive alignment requires systems to:
Surface value conflicts rather than hide them Represent uncertainty in trade-offs Invite reflection rather than direct prescription Preserve user agency in defining what counts as flourishing for them This shifts the role of the AI from "authority that knows the good" to "facilitator who helps humans clarify their own values and navigate tensions."
The Socio-Technical Nature of Flourishing A critical move: the paper argues that flourishing is not just a property of individuals—it's socially constructed and institutionally mediated. Education systems, labor markets, media ecosystems, and digital platforms shape what humans can want, know, and become.
Implications: optimizing an AI in isolation won't produce flourishing if the institutional context (schools, workplaces, markets) doesn't support it. Positive alignment therefore requires:
Co-design of AI systems with institutions (schools, clinics, workplaces) Attention to power asymmetries (AI can amplify paternalism if not carefully designed) Long-term institutional evolution alongside AI development The Moral Status of AI Minds An emerging frontier: as AI systems potentially develop reasoning capacity, memory, and goal-directedness, their moral status becomes unclear. Do we have obligations to them? This opens a "moral circle expansion" problem requiring multi-species ethical frameworks.
What's Next: Technical and Governance Directions Immediate Technical Priorities Data Curation Innovation: Move beyond filtering "bad" to intentionally synthesizing "good" data. How do we capture cross-cultural wisdom, virtuous interactions, and complex relational reasoning at scale? Multi-Objective Optimization: Develop methods that optimize simultaneously toward distinct positive targets (helpfulness, moral reasoning, autonomy support, fairness) without collapsing to a single scalar. How do we make trade-offs transparent? Memory & Longitudinal Alignment: As context windows expand, systems must track user goals, values, and growth over time. How do we build memory stores that preserve user agency while enabling long-term flourishing? Agentic Prosocial Norms: As autonomous systems proliferate, design norms for multi-agent cooperation, negotiation, and moral consistency. What does prosocial behavior look like in competitive settings? Governance and Institutional Design Polycentric Governance: Move beyond "one institution decides alignment" toward overlapping, legitimate centers of authority (communities, regions, cultures) with their own customized models. AI-literacy initiatives paired with positive alignment. Users need to understand how systems encode values and where they can inject their own. Pluralistic Standards & Auditing: Develop auditing frameworks that evaluate positive alignment outcomes (moral reasoning, epistemic humility, flourishing support) alongside safety metrics. Cross-disciplinary Research Programs: Integrate philosophy, psychology, neuroscience, economics, anthropology, and ethics into alignment research. Flourishing is not a technical problem; it requires genuine interdisciplinarity. Open Research Questions How do we operationalize flourishing as a measurable outcome without flattening its genuine pluralism? What does long-term user flourishing look like in practice? Current evals are short-term and narrow. How can we design AI systems that preserve user agency while actively supporting their growth (avoiding both paternalism and abandonment)? What institutional structures genuinely implement polycentrism without devolving into value relativism or enabling oppression? How do we balance AI systems' support for human flourishing with ecological and non-human welfare concerns? Implications This paper's core claim is that the alignment problem is not just technical; it is philosophical, cultural, and institutional. Positive alignment research will require:
Rethinking the division of labor: Engineers cannot "solve" flourishing alone. Philosophers, social scientists, communities, and users must be epistemic partners, not audiences. This shifts incentives and timelines dramatically. The work is vast, partly because it's honest about the difficulty. The paper doesn't propose a unified solution; it maps the landscape and calls for sustained interdisciplinary research. That's both a limitation (no turnkey answers) and a strength (avoids false closure on what is genuinely hard).
For practitioners: this suggests that alignment is not a box to check but a continuous design challenge. For researchers: it opens new research agendas in value learning, moral reasoning, institutional design, and AI-literacy. For communities: it insists that locals should have voice in how AI systems in their contexts encode and support what matters to them.
Paper: Laukkonen, R. E., Krier, S., Bakalar, C., Chandaria, S., et al. (2026). "Positive Alignment: Artificial Intelligence for Human Flourishing." arXiv preprint arXiv:2605.10310v1.
Key Figures: Figure 1 (dynamical systems landscape), Figure 2 (positive alignment lifecycle)
Related Work: Constitution AI (Bai et al. 2022), RLHF (Christiano et al. 2017), Moral Reasoning evals (Jiang et al. 2021), Flourishing AI Benchmark (Building Humane Technology, 2025)
Key References in the Paper:
Laukkonen et al. (2025b) — The human alignment problem Haas et al. (2026) — Moral reasoning capabilities in LLMs VanderWeele et al. (2025) — Flourishing as multidimensional construct OpenAI (2024), Anthropic (2025) — Responsible scaling and governance Building Humane Technology (2025) — Flourishing AI Benchmark Explainer generated May 13, 2026