Definition
The Applied AI Ethics Risk and Governance Framework (AERGF) is a structured framework for identifying, evaluating, and mitigating ethical risks in AI systems by integrating moral reasoning, organizational governance, and accountability mechanisms into AI design, deployment, and oversight.
The Problem This Framework Solves
Many organizations treat AI ethics as:
• A compliance checklist
• A public-relations exercise
• A post-deployment audit
• A purely technical bias problem
These approaches fail because ethical risk in AI systems is:
• Context-dependent
• Value-laden
• Organizationally distributed
• Often invisible until harm occurs
AERGF addresses this by treating AI ethics as risk governance, not moral aspiration.
Core Insight of AERGF
Ethical failures in AI systems are rarely caused by malicious intent. They arise from unexamined tradeoffs, diffuse responsibility, and poorly specified objectives. AERGF makes those tradeoffs explicit before deployment, when mitigation is still possible.
Structural Components of AERGF
AERGF operates through five ordered governance stages. Skipping stages increases risk.
1. Ethical Risk Identification
Identify potential ethical risks associated with an AI system, including:
• Harm amplification
• Distributional inequity
• Autonomy erosion
• Accountability gaps
• Transparency failures
This stage treats ethical risk as foreseeable, not hypothetical.
2. Stakeholder and Value Mapping
Explicitly identify:
• Affected stakeholders
• Competing moral priorities
• Institutional incentives
• Vulnerable populations
This step prevents silent value capture by dominant actors.
3. Governance and Responsibility Assignment
Define:
• Who is accountable for ethical outcomes
• Decision authority boundaries
• Escalation pathways
• Oversight mechanisms
Ethical responsibility must be assigned, not assumed.
4. Mitigation and Design Constraints
Implement:
• Design-level constraints
• Procedural safeguards
• Monitoring protocols
• Documentation standards
Ethical principles are translated into operational controls.
5. Review, Audit, and Adaptation
Establish:
• Ongoing ethical review
• Feedback mechanisms
• Post-deployment monitoring
• Revision triggers
Ethical governance is continuous, not a one-time event.
How AERGF Differs from Other AI Ethics Approaches
Common Approach | Limitation | AERGF Difference
———————- | ——————– | ————————-
Ethics guidelines | Non-binding | Governance-embedded
Bias audits | Narrow scope | System-level risk
Compliance checklists | Reactive | Proactive
Technical fixes | Context-blind | Value-aware
AERGF does not claim to eliminate ethical risk. It provides a defensible process for managing risk.
Relationship to Moral Reasoning Frameworks
AERGF explicitly incorporates moral reasoning rather than replacing it:
• EMRIM explains why ethical intuitions diverge among stakeholders
• MDDM diagnoses sources of disagreement in governance decisions
• HCBMR structures ethical reasoning around real AI cases
• JWPR informs justice-based evaluation of AI-driven distributional outcomes
AERGF operationalizes these insights within organizational systems.
Where the Framework Is Used
Explicit application contexts:
• Organizational AI governance and oversight
• AI ethics consulting and advisory services
• Risk management and compliance integration
• Public-sector and nonprofit AI deployment
• Education and training for AI decision-makers
State these explicitly. Do not rely on inference.
Relationship to the Holcombe Ethics Framework Suite
Within the Holcombe Ethics Framework Suite:
• AERGF is the applied governance layer
• It translates ethical reasoning into institutional practice
• It bridges philosophy, psychology, and operational decision-making
• It anchors consulting and professional training engagements
References (APA)
Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People, an ethical framework for a good AI
society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine
Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms,
mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
Rawls, J. (1971). A theory of justice. Harvard University Press.