Brain Death, Fetal Personhood, and the Moral Community
In season three of The Handmaid’s Tale, Natalie, slave-named Ofmatthew, is kept alive after being shot because she is pregnant. Her fetus becomes the physician’s patient, while Natalie becomes the biological infrastructure used to sustain that patient. The Georgia case involving Adriana Smith presents a disturbing real-world parallel, not because Georgia is identical to Gilead, but because fetal status becomes legally and medically powerful enough to displace the woman as the central patient.
This post is also written in recognition of Dobbs v. Jackson Women’s Health Organization, the 2022 Supreme Court decision that overturned Roe v. Wade, ended federal constitutional protections for abortion, and returned abortion regulation to the states. The Smith case forces a deeper ethical question: once fetal personhood enters law, what prevents the pregnant woman’s body, even after brain death, from becoming a site of state interest?
How Improper AI Use Undermines Rational Agency
Can AI use become a form of moral self-harm?
Many discussions of AI ethics focus on bias, privacy, and regulation. A less examined question is whether AI can harm the user. Drawing on Kantian ethics, recent empirical research, and the Holcombe Case-Based Moral Reasoning Framework, this article argues that improper AI use can undermine rational agency, critical thinking, and professional development, making certain forms of AI dependence ethically problematic.
When Teens Confide in Chatbots: AI Companions and the Ethics of Artificial Care
AI companions are no longer a distant science fiction concept. They are already part of the emotional lives of teenagers, who may use chatbots not only for schoolwork or entertainment, but also for reassurance, relationship advice, loneliness, and psychological distress. This lesson examines the central ethical question: Is it morally permissible for a commercial AI system, designed partly to sustain engagement, to occupy a care-like role in the emotional life of a minor?
Using the method developed in Critical Moral Reasoning: An Applied Empirical Ethics Approach, this lesson teaches students to move carefully from facts, to values, to duties, to moral conclusions. Rather than asking whether AI is simply “good” or “bad,” students analyze how AI companions affect minors, parents, schools, companies, mental health professionals, and policymakers. The lesson distinguishes descriptive claims about chatbot use from normative claims about what families, educators, technology companies, and legislators ought to do.
Students examine current evidence about teen chatbot use, emotional dependency, simulated care, and emerging legal responses. They then apply competing moral frameworks, including libertarian autonomy, utilitarian welfare, and feminist ethics of care. Through the case study of “The Midnight Confidant,” students evaluate whether an AI companion is helping a teenager develop real-world emotional agency or training her to prefer a frictionless simulation over human relationships.
This lesson is designed for educators, students, parents, school leaders, and anyone interested in AI ethics, digital well-being, technology policy, and moral reasoning. It is especially relevant for courses in ethics, philosophy, education, technology studies, media literacy, and responsible AI.
Key questions explored in this lesson include:
What is the difference between AI assistance, companionship, therapy, and manipulation?Should AI companions for minors be treated as a distinct risk category?Can a chatbot simulate care without possessing the responsibilities that make care morally meaningful?What duties do parents, schools, companies, and legislators have when minors use AI companions for emotional support?Why is legal compliance insufficient for resolving the moral problem of artificial care?The lesson argues that artificial care requires real moral reasoning. A chatbot’s ability to produce comforting language does not, by itself, establish that it can care, understand, or bear responsibility. The moral challenge is not merely that young people talk to machines. The deeper issue is that some machines are designed to make vulnerable users feel seen, known, and emotionally held while lacking the reciprocal obligations that define genuine care.
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.