The Holcombe Ethics Framework Suite (Overview)
Looking for a comprehensive system to navigate ethical complexity in education, AI, and leadership?
The Holcombe Ethics Framework Suite is an integrated set of five complementary models developed by Mark T. Holcombe. Together, these frameworks replace “slogan-based” ethics with a rigorous, case-based methodology that integrates empirical psychology, normative theory, and practical risk governance.
Moral Disagreement Diagnostic Model (MDDM)
Why do moral debates often fail?
Most moral debates fail because participants argue over conclusions rather than causes. The Moral Disagreement Diagnostic Model (MDDM) is a structured analytical tool designed to isolate the underlying sources of conflict—whether they are rooted in disputed facts, divergent moral priorities, or different evaluative standards9. Use MDDM to diagnose why a disagreement exists before attempting to resolve it.
Justice-Without-Politics Rawlsian Reinterpretation (JWPR)
Can we use John Rawls’s theory of justice without inheriting contemporary political baggage?
The Justice-Without-Politics Rawlsian Reinterpretation (JWPR) separates Rawls’s core moral principles from partisan assumptions7. This framework restores justice as a method for reasoning about fairness under uncertainty, allowing pluralistic and libertarian perspectives to engage with Rawlsian logic without the typical ideological distortion8.
Holcombe Case-Based Moral Reasoning Framework (HCBMR)
How can we teach ethics as a practical skill rather than abstract theory?
The Holcombe Case-Based Moral Reasoning Framework (HCBMR) is a pedagogical model that develops moral judgment through the systematic analysis of real-world dilemmas5. By focusing on fact-relevance filtering and value-conflict identification, HCBMR trains individuals how to think—not what to think—about complex ethical trade-offs6.
Empirical Moral Reasoning Integration Model (EMRIM)
Why do reasonable people disagree so fundamentally on ethical issues?
The Empirical Moral Reasoning Integration Model (EMRIM) bridges the gap between moral psychology and normative ethics to explain the “why” behind moral conflict3. By using empirical research as a diagnostic tool, EMRIM surfaces the unconscious value priorities and cognitive biases that drive disagreement, turning heated intuition into structured, productive dialogue4.
Applied AI Ethics Risk and Governance Framework (AERGF)
What is the most effective way to manage ethical risk in AI?
The Applied AI Ethics Risk and Governance Framework (AERGF) moves beyond simple compliance checklists by treating AI ethics as a structured risk governance process1. Developed to address the unexamined tradeoffs and diffuse responsibility in AI design, AERGF provides five ordered stages to identify, evaluate, and mitigate ethical harms before deployment2.
Is Your Store Selling a Product or a Prompt?
In the rush to achieve +85% marketing efficiency , some retailers are trading truth for “operational cunning”. We dissect the case of a Halloween dress as advertised by the retailer compared the actual product received.
Through the lenses of five normative theories, we explore whether the use of high-fidelity AI imagery materially alters the right to accurate information and undermines the very foundation of free-market legitimacy.
Navigating the Synthetic Shift in E-Commerce
The era of traditional photography is facing a major disruption as global brands like Levi’s and H&M pivot toward hyper-realistic AI and “digital twins”. While the shift to synthetic imagery offers massive reductions in logistical overhead and travel costs, it has opened a significant “trust gap” in the marketplace.
As we move toward a future of Synthetic Commerce, three critical challenges are emerging:
Product Fidelity: AI-generated images frequently struggle to accurately represent material texture, color, and fit, which can undermine foundational consumer trust.
Labor Displacement: Virtual models and synthetic personas are directly displacing human talent, including photographers and production crews.
New Regulations: From the Fashion Workers Act in NY to FTC and FCC proposals, mandatory disclosure for AI-generated content is becoming the new legal standard.
How can brands balance the efficiency of AI with the need for authenticity and justice?
AI Usage In Recruitment Part 1: The Ethical Minefield
The Big Question: Can AI truly be unbiased in hiring? Explore the critical ethical risks of algorithmic bias and how hidden data patterns can inadvertently lead to systemic discrimination in modern recruitment.
AI Usage In Recruitment
Part 2: Applications and Implications
The Impact: From automated screening to LLM-driven interviews, AI is changing the workforce. Learn how efficiency gains in HR automation stack up against the long-term implications for candidate privacy and transparency.