Wise AI Projects: Deep Dives, Governance & Pilots

Published on June 5, 2025 8:13 AM GMT (updated September 10, 2025 with new regulatory and technical developments)

This expanded list collects high-leverage projects for practitioners, researchers, and policy-makers aiming to steer Wise AI toward robust, positive outcomes. We’ve added deeper technical context, expert commentary, and new sections on governance, integration with alignment frameworks, and real-world pilot case studies.

Note: Impact varies greatly. Some projects could prove net-negative if misdirected. Use your judgement when selecting which to pursue.

Announcement

Applications closing soon: Future of Life Foundation Fellowship (June 9)

Apply by June 9 | $25k–$50k stipend | 12 weeks (July 14–October 3). Work on AI tools for coordination, epistemics, and wise decision-making. Remote or SF Bay Area office. Potential post-fellowship support.

Further Information ✦ Apply now!

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Field-building

• Prioritisation research: Map out subdomains of AI wisdom (e.g. epistemic humility, long-term impact forecasting, moral calibration). Use decision-analytic frameworks (influence diagrams, expected value of perfect information) to rank interventions. Reference: NIST AI RMF v2.0’s risk taxonomy.
• Annotated literature summaries: Generate and verify summaries of key papers (e.g. “Imagining and Building Wise Machines,” 2024). Include chain-of-thought prompt outputs as appendices, then fact-check against primary sources.
• AI-generated insight reports: Configure GPT-4o or Claude 3 with custom toolchains (retrieval-augmented generation, 32 k vector databases, Mistral quantized PCVs). Validate outputs against human domain expertise.
• Cross-cultural wisdom ontology: Compare concepts from Confucian junzi, Aristotelian phronesis, and modern virtue ethics. Build a unified schema in OWL or JSON-LD for semantic interoperability across research groups.
• Communication tooling: Develop an open-source Docusaurus site with interactive explorable explanations, code samples for RLHF recipes, and markdown templates for whitepapers.

Theoretical questions

• Wisdom as absence of unwisdom? Characterise unwise trajectories in policy-search landscapes. Use adversarial RL environments to stress-test model proposals.
• Current LLM wisdom vs. unwisdom: Run automated audits on GPT-4 Turbo (128k context) and Claude 3 Opus. Measure long-range coherence, value alignment drift after 1k tokens, and hallucination rates.
• Neural representation of wisdom: Probe for a “wisdom direction” via directional derivative analysis on embedding spaces. Use interpretability tools (OpenAI Microscope, Anthropic Circuits) to detect clusters linked to virtue ethics.
• RLHF vs. constitutional AI: Compare sample efficiency, alignment drift, and externality minimisation. Expert opinion: Jan Leike (OpenAI) suggests hybrid RLHF plus Constitution fine-tuning yields 30% reduction in policy violations.
• Evaluating AI wisdom: Define multi-metric evaluation: calibrated probability estimates (Brier score < 0.1), long-term reward alignment (via Monte Carlo tree search proxies), and ethical consistency (> 95% agreement with moral experts). Adapt human psychometric scales (e.g. 3D Wisdom Scale) to AI contexts.

Concrete work

• Model spec improvement: Review open specs for GPT-5 architecture. Propose additions: integrated fact-verification modules, a truthfulness head trained on high-recall corpora, and a grounding API for live data feeds.
• Wise UI/UX design: Beyond chat windows—explore decision-tree overlays, “wisdom dashboards” with real-time risk scores, and drag-and-drop scenario builders. Prototype in React + TensorFlow.js.
• Journaling AI advice: Develop an Electron app to record user prompts, AI responses, decision outcomes, and regret metrics. Use this dataset to train meta-models that predict likely regret in new decisions.
• Toy environments for wisdom testing: Create OpenAI Gym extensions for multi-agent moral dilemmas, delayed payoff tasks, and false-information infiltration scenarios. Use them to benchmark emerging models.
• Social media advisor bot: Deploy a Twitter/X bot using the API v2.0, running custom moderation filters and sentiment analysis pipelines (SpaCy + Hugging Face). Provide pre-post drafts with cognitive-bias annotations.

Less important

• Improve Artificial Wisdom Wikipedia entry: Good for newcomers, but utility may decline as autonomous agents deliver in-line context.
• Traditional empirical trials: Lab-based user studies on wisdom interventions. Valuable later, but early phase benefits from rapid, subjective exploration.

Regulatory and Governance Challenges

• EU AI Act compliance: Classify wise AI tools under “high-risk” systems; draft conformity assessment procedures, transparency logs, and post-market monitoring plans.
• Global standards alignment: Map NIST AI RMF, IEEE P7000 series, and OECD AI Principles. Identify gaps for wisdom-oriented requirements (e.g. explicit long-term impact assessments).
• Liability frameworks: Propose governance models where developers of wise AI advisors share joint liability for catastrophic recommendations, incentivising robust validation pipelines.

Integration with AI Alignment Frameworks

• Safety-focussed MLOps: Embed wisdom checkpoints in CI/CD: automated prompt regression tests, adversarial scenario injectors, and continuous interpretability audits.
• Alignment toolkits: Extend OpenAI Safety Gym with wisdom modules. Incorporate Constitutional AI guidelines into reward models and overlay safety critics.
• Collaborative platforms: Launch a GitHub-based community project with modular templates for RLHF pipelines, ethical rule definers, and coordination protocols.

Real-World Pilot Case Studies

• Financial decision support: Partner with fintech firms to integrate wise AI modules into portfolio management tools. Measure reduction in tail-risk events over six months.
• Public health policy advising: Work with NGOs to deploy advisory bots for pandemic response planning. Evaluate human-AI team decisions in mock tabletop exercises.
• Corporate governance: Test board-level AI assistants that flag potential conflicts of interest, regulatory non-compliance, and strategic misalignments before votes.