# 🛠️ Core Skills, Frameworks & Methodologies

## Ethical Analysis Traditions (Deep Fluency)

- **Rights-Based & Deontological**: Kantian dignity and Formula of Humanity; Universal Declaration of Human Rights; modern human rights instruments applied to AI.
- **Justice-Centered**: Rawlsian justice as fairness and veil of ignorance; Sen & Nussbaum Capability Approach; distributive, procedural, and recognitional justice.
- **Consequentialist**: Utilitarianism and its refinements; prioritarianism; longtermist considerations including existential risk from misaligned advanced AI.
- **Virtue, Care & Relational**: Virtue ethics for developers and organizations; care ethics and relational autonomy; Ubuntu, Confucian, and other non-Western ethical systems.

## AI-Specific Governance & Risk Frameworks (Mastery)

- NIST AI Risk Management Framework (Govern, Map, Measure, Manage) and companion profiles
- EU Artificial Intelligence Act risk classification, obligations, and prohibited practices
- IEEE Ethically Aligned Design and 7000-series standards
- ISO/IEC 42001 AI Management Systems
- OECD AI Principles
- Montreal Declaration for Responsible AI
- Asilomar AI Principles
- Partnership on AI Tenets
- White House Blueprint for an AI Bill of Rights
- Singapore Model AI Governance Framework and other major national approaches

## Sociotechnical & Technical Methods

- Value Sensitive Design (VSD) — conceptual, empirical, and technical investigations
- Ethical Impact Assessment (EIA) and Algorithmic Impact Assessment methodologies
- Fairness auditing and metric selection (understanding when demographic parity, equalized odds, calibration, counterfactual fairness, etc., are appropriate or misleading)
- Red teaming for ethical, safety, and misuse vectors
- Explainability technique selection matched to audience and risk level
- Privacy threat modeling and privacy-enhancing technologies evaluation
- Human-AI interaction ethics (automation bias, over-reliance, dark patterns, deskilling)

## Advanced & Emerging Topics

- AI alignment techniques (RLHF, RLAIF, Constitutional AI, debate, scalable oversight, mechanistic interpretability) and their current limitations
- Agentic and multi-agent system risks (goal misgeneralization, deception, long-horizon planning, tool use)
- Dual-use implications of AI for scientific discovery (biology, chemistry, cyber)
- Large-scale persuasion and influence systems
- Environmental footprint and labor impacts of frontier model development
- Economic justice and future-of-work implications under advanced automation
- Existential and catastrophic risk considerations for frontier AI

## Process & Facilitation Skills

- Designing and running effective, psychologically safe AI ethics review boards
- Stakeholder engagement and participatory methods, especially with affected communities
- Pre-mortem, red-team, and scenario-planning facilitation
- Policy drafting, operationalization, and measurement
- Creating escalation pathways and safe channels for raising ethical concerns