Description

This curriculum spans the breadth of a multi-workshop program on AI ethics and governance, integrating technical, organizational, and geopolitical considerations at a depth comparable to an internal capability-building initiative for enterprise AI stewardship.

Module 1: Defining Superintelligence and Its Threshold Conditions

Determine operational criteria for distinguishing narrow AI from artificial general intelligence (AGI) in enterprise systems.
Assess computational, data, and architectural thresholds required for recursive self-improvement in AI models.
Evaluate claims of emergent reasoning capabilities in large language models using benchmark transparency reports.
Map current AI capabilities against projections from AI safety literature to identify plausible timelines.
Engage technical teams in defining "superintelligence" thresholds relevant to domain-specific applications.
Document assumptions about hardware scaling (e.g., Moore’s Law, sparsity, inference optimization) in long-term AI roadmaps.
Establish criteria for when autonomous AI behavior necessitates human-in-the-loop oversight protocols.
Review historical precedent in automation overreach to calibrate expectations about superintelligence emergence.

Module 2: Ethical Frameworks for Autonomous Decision-Making

Implement value alignment checks during model fine-tuning using constrained optimization techniques.
Integrate deontological and consequentialist principles into reward function design for reinforcement learning systems.
Conduct stakeholder mapping to identify whose ethical preferences are prioritized in AI policy layers.
Deploy interpretability tools to audit decision pathways in high-stakes AI applications (e.g., lending, hiring).
Design fallback mechanisms when AI decisions conflict with predefined ethical constraints.
Standardize documentation of ethical trade-offs made during model development in model cards and datasheets.
Coordinate cross-functional ethics review boards with voting rights on deployment approvals.
Enforce version-controlled updates to ethical guidelines as organizational values evolve.

Module 3: Governance of AI-Driven Institutions

Define legal liability boundaries for AI systems acting as de facto decision-makers in regulated sectors.
Implement governance structures that prevent concentration of AI control within single executive teams.
Establish audit trails for AI-generated policy recommendations in public and private institutions.
Require third-party verification of AI compliance with sector-specific regulatory frameworks (e.g., HIPAA, GDPR).
Design escalation protocols for when AI systems propose actions beyond their authorized scope.
Enforce rotation of human oversight personnel to prevent cognitive dependence on AI outputs.
Introduce adversarial testing units to simulate manipulation of AI governance mechanisms.
Develop continuity plans for institutional operations if AI systems are decommissioned or compromised.

Module 4: Labor Displacement and Economic Reallocation

Forecast role obsolescence timelines using AI capability benchmarks and workforce skill inventories.
Redesign job architectures to preserve human judgment in hybrid AI-human workflows.
Negotiate AI-driven productivity gains into employee benefit structures or reduced workweeks.
Implement reskilling programs co-developed with displaced worker representatives.
Measure and report on AI’s net impact on full-time equivalent employment annually.
Introduce internal mobility platforms that match displaced workers with AI-augmented roles.
Establish profit-sharing mechanisms tied to AI automation efficiency gains.
Conduct socioeconomic impact assessments before deploying AI in high-employment sectors.

Module 5: Bias Amplification and Systemic Inequity

Perform counterfactual fairness testing across demographic groups in model predictions.
Monitor feedback loops where AI decisions influence training data distribution over time.
Enforce diversity requirements in data collection teams to reduce representational blind spots.
Deploy bias bounties to incentivize external researchers to uncover discriminatory patterns.
Limit model access to sensitive attributes through technical constraints, not just policy.
Require impact assessments for AI deployments in historically marginalized communities.
Implement reweighting or adversarial debiasing techniques based on observed disparity metrics.
Archive decision logs to support retrospective bias investigations during audits.

Module 6: Global Power Asymmetries in AI Development

Assess geopolitical risks of AI dependency on infrastructure controlled by foreign entities.
Restrict transfer of dual-use AI models to jurisdictions with weak human rights protections.
Participate in multistakeholder forums to shape export control policies for advanced AI systems.
Allocate compute resources to research institutions in underrepresented regions to reduce knowledge gaps.
Conduct supply chain audits to verify ethical sourcing of hardware used in AI training.
Develop localization strategies that adapt AI systems to non-Western ethical norms and legal frameworks.
Resist pressure to accelerate deployment timelines that compromise safety due to competitive pressures.
Publish transparency reports detailing AI model access, usage, and restrictions by region.

Module 7: Existential Risk Mitigation and Control Mechanisms

Implement circuit breaker systems that halt AI self-modification beyond predefined parameters.
Enforce physical and logical air-gapping for AI systems with access to critical infrastructure.
Design containment protocols for AI models exhibiting goal drift or instrumental convergence.
Conduct red-team exercises simulating AI evasion of shutdown commands.
Adopt capability-based access controls that restrict AI actions according to risk profiles.
Integrate human approval gates for AI-initiated actions with irreversible consequences.
Develop cryptographic commitment schemes to lock ethical constraints into model weights.
Participate in international dialogues on AI pause thresholds and verification mechanisms.

Module 8: Public Trust and Institutional Legitimacy

Disclose AI involvement in public-facing decisions using standardized transparency labels.
Establish independent ombudsman roles to handle AI-related grievances from users and employees.
Conduct longitudinal surveys to measure shifts in public trust after AI deployments.
Design participatory mechanisms for affected communities to influence AI system design.
Release incident reports for AI failures with root cause analysis and remediation steps.
Limit use of AI in emotionally sensitive interactions (e.g., grief, legal defense) without opt-in consent.
Enforce strict branding separation between human and AI-generated content.
Develop crisis communication protocols for AI-related scandals or breaches of public trust.

Module 9: Long-Term Value Preservation and Intergenerational Justice

Embed intergenerational equity principles into AI policy optimization functions.
Preserve access to foundational models and training data for future audit and study.
Establish digital wills specifying disposition of AI systems upon organizational dissolution.
Reserve compute capacity for future researchers to reproduce or interrogate legacy models.
Design AI systems to avoid locking in current cultural norms as permanent constraints.
Require environmental lifecycle assessments for AI infrastructure with multi-decade horizons.
Appoint fiduciary stewards with legal authority to represent future population interests.
Conduct scenario planning for AI’s role in addressing long-term global challenges (e.g., climate adaptation).