Most LLM misalignments are choices, not fate: problems often come from data, objectives, and deployment decisions, not just model scale. Adopting a Flourishing–Justice–Autonomy approach—pluralistic evaluation, transparency, and participatory governance—would reduce harms and address economic incentive failures by treating alignment as a public‑good, ongoing process.

Main Finding

The paper (Naseem et al., Cognitive Computation 2026) argues that prevailing LLM alignment practice—centred on Harmless–Helpful–Honest (HHH) via post‑training fixes (e.g., RLHF, static safety filters)—is operationally brittle and often produces overcautious, shallow, or culturally biased outputs. The authors propose a normative and operational shift to a Flourishing–Justice–Autonomy (FJA) framework that (1) broadens alignment objectives beyond safety/helpfulness, (2) emphasises pluralism and user agency, and (3) prioritises inference‑time, participatory, and dynamic reward mechanisms to better balance safety, usefulness, cultural sensitivity, and autonomy.

Key Points

• Limitations of HHH arise largely from operational choices (rigid post‑training filters, static reward models, narrow annotator pools), not just principle-level goals. These choices cause:
  • Over‑alignment: false refusals and refusal of benign queries.
  • Reasoning deficits: safe‑sounding but shallow or incorrect reasoning.
  • Cultural misalignment: outputs that reflect dominant/Western norms and marginalise minority perspectives.
  • Vulnerability to adversarial “jailbreaks” because static filters don’t generalise.
  • An “alignment tax”: loss of creativity/expressivity.
  • Scalability problems: annotator coverage and governance don’t scale with model size.
• FJA framework:
  • Flourishing: alignment should support long‑term human well‑being (capabilities, learning, creativity), not only turn‑level helpfulness.
  • Justice: amplify marginalized voices, pluralistic evaluation, and participatory governance to reduce systemic bias.
  • Autonomy: preserve user agency — transparent trade‑offs, configurable objectives, and challengeable constitutions.
• Operational mechanisms proposed:
  • Inference‑time objective extraction and alignment (allowing context‑sensitive trade‑offs).
  • Participatory constitutions (norms defined with diverse stakeholders; user‑challengeable rules).
  • Dynamic reward/judge models that score candidate outputs on pluralistic objectives at inference time.
  • Multi‑objective optimization to preserve creativity under safety bounds.
  • Pluralistic benchmarks and new metrics (pluralistic sensitivity, refusal calibration, reasoning integrity, creativity balance).
  • Hybrid pipelines combining RLHF, Constitutional AI (CAI), Direct Preference Optimization (DPO), adversarial training, and automated preference learning.
• Evaluation and transparency:
  • Advocate richer evaluation suites beyond toxicity/helpfulness to measure context‑sensitive harms, false refusals, reasoning depth, and cultural sensitivity.
  • Explainability features (e.g., flagged provenance: “this answer relies on Western sources”) and participatory auditing.
• Authors position FJA as a normative and operational roadmap rather than a fully implemented system; they call for benchmarking FJA implementations against HHH baselines.

Data & Methods

• Paper type: position / conceptual paper (no original empirical dataset or experiments reported).
• Reviewed existing alignment techniques and failure modes: RLHF, CAI (Constitutional AI), DPO, RLAIF, red‑teaming/adversarial training, and hybrid approaches.
• Synthesised qualitative examples and documented operational failure cases (medical refusals, shallow legal reasoning, cultural erasure, jailbreaks).
• Proposed operational mappings (table mapping specific alignment failures to FJA pillars and mechanisms) and a high‑level inference‑time decision pipeline (objective extraction → constitution validation → dynamic reward weighting → candidate search).
• Proposed evaluation metrics and design principles for building and auditing systems aligned to FJA.
• Cites prior empirical and theoretical work (e.g., limitations of RLHF, CAI, reasoning‑aware reward model work) but does not present new quantitative results.

Implications for AI Economics

• Product differentiation and market segmentation:
  • FJA enables configurable, pluralistic alignment profiles (e.g., culturally attuned, autonomy‑preserving versions), encouraging product variants targeted to different user groups and regions. This could increase willingness‑to‑pay for bespoke, trustworthy models and create niche markets (e.g., education, indigenous language support).
• Costs and incentives:
  • Short term: participatory alignment, richer evaluation, and constitutions raise governance and coordination costs (stakeholder engagement, auditing, bespoke curation).
  • Medium term: inference‑time alignment and automated preference learning may reduce reliance on massive annotator pools, shifting costs toward compute and engineering (dynamic reward models, runtime search), changing the structure of recurring costs.
  • The “alignment tax” trade‑off affects product utility — overly conservative models reduce user value and may shrink market adoption; FJA aims to recover utility while managing risk, potentially improving consumer surplus.
• Labour and markets for human oversight:
  • Demand shifts from bulk annotators toward higher‑skilled, diverse domain experts, community liaisons, and auditors (participatory constitution contributors), altering labor composition and wage structure in alignment supply chains.
• Regulation, liability, and compliance economics:
  • Greater transparency, provenance flags, and auditability lower information asymmetries between vendors, users, and regulators; this can reduce legal risk premiums and insurance costs but may expose firms to new compliance demands.
  • Participatory constitutions and localized alignment could ease regulatory acceptance in jurisdictions sensitive to cultural or human‑rights concerns, lowering market entry frictions internationally.
• Externalities and inequality:
  • If implemented inclusively, FJA can reduce cultural invisibility and informational inequality (better service for marginalized populations). Conversely, if participatory processes are captured by well‑resourced actors, pluralism could be simulated and deepen incumbent advantages.
• Security and systemic risk:
  • Moving to inference‑time, dynamic defenses and hierarchical safeguards may reduce some jailbreak risks but introduce new attack surfaces (runtime judge models, objective extraction). The economic cost of security failures (misinformation, safety incidents) remains a material risk factor affecting firm valuations and sector regulation.
• Competitive advantage and innovation:
  • Firms that successfully operationalize FJA-style alignment may obtain competitive advantage via higher trust, better user retention, and access to regulated markets (healthcare, education). However, the engineering complexity could be a barrier to entry, consolidating market power among well‑resourced incumbents unless open standards and community governance reduce barriers.
• Measurement and metrics:
  • Adoption of the proposed pluralistic sensitivity and refusal calibration metrics will influence procurement, certification, and procurement contracts; these metrics create new quantifiable signals in the market that will affect contracting, audits, and investment decisions.

Overall, the FJA proposal reframes alignment as a multidimensional public‑good and governance problem with nontrivial economic trade‑offs: higher upfront governance costs and engineering complexity against potential gains in trust, market access, product utility, and reduced externalities. Policymakers, firms, and market designers will need to weigh these trade‑offs when incentivising or mandating pluralistic, participatory alignment practices.

Reference: Naseem, U., Chakraborty, T., Chang, K.-W., Dras, M., Nakov, P., Peng, N., & Poria, S. (2026). LLM Alignment should go beyond Harmlessness–Helpfulness and incorporate Human Agency. Cognitive Computation. https://doi.org/10.1007/s12559-026-10568-9