Mark T Holcombe

Author: Mark T Holcombe

Birmingham’s Data Center Debate: What City Council and the IDB Got Right, and Where They Failed

Modern data center infrastructure near an urban neighborhood, representing Birmingham’s debate over AI infrastructure regulation and public accountability.

What happens when a city regulates AI infrastructure but fails to give affected communities a meaningful role in deciding where its burdens fall?
Birmingham’s new data center ordinance shows that city leaders recognized the need to regulate hyperscale AI infrastructure, but the process still left major ethical failures unresolved. Discussed is what Birmingham City Council and the Industrial Development Board got right, where they failed on public accountability, and why data center governance must address burden shifting, siting equity, and meaningful community participation.
This analysis is best understood through Mark T. Holcombe’s Applied AI Ethics Risk and Governance Framework (AERGF), which treats AI ethics as a governance problem involving stakeholder impacts, institutional responsibility, risk mitigation, and ongoing accountability rather than a narrow question of technical compliance or economic development.

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.

AI Is Firing Employees. Who Is Morally Responsible?

Artificial intelligence is no longer limited to hiring decisions. Increasingly, AI systems monitor worker behavior, generate performance scores, recommend discipline, and in some cases contribute directly to employee termination. When an algorithm recommends firing a worker, who bears moral responsibility?

This article examines automated termination through the lenses of John Rawls’s Justice as Fairness, Robert Nozick’s libertarian theory of self-ownership, and Virginia Held’s ethics of care. Drawing on real-world examples from Amazon Flex and contemporary AI governance debates, it explores whether efficiency is enough to justify decisions that can cost workers their livelihoodAs organizations adopt AI-driven management systems, the ethical question becomes increasingly urgent: Should algorithms assist human judgment, or replace it?

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.

When AI Infrastructure Meets Utility Reform, What Alabama Gets Right, and What It Still Misses

The modern “cloud” is anything but weightless. Behind every AI interaction lies a growing physical infrastructure, data centers that consume vast amounts of water, electricity, and land. In Alabama, lawmakers are beginning to confront this reality through the bipartisan Alabama Affordability Protection Plan, a set of reforms designed to prevent everyday residents from subsidizing the rapid expansion of AI infrastructure.

This article examines how the legislation attempts to rebalance costs, enforce accountability, and align incentives with public benefit. It also analyzes the overlooked risks introduced by SB71, a regulatory constraint that may limit Alabama’s ability to respond to the environmental and public health consequences of large-scale data center growth. The result is a policy landscape that advances economic fairness while potentially constraining environmental protection, raising a deeper question: can states meaningfully govern the “physical weight” of AI without full regulatory autonomy?

Drawing from emerging research on digital infrastructure and environmental justice, this analysis situates Alabama as a critical case study in the broader challenge of governing AI’s material footprint.

Why Automating the “Doing” Undermines Real Expertise

Preview Text Can expertise develop without hands-on effort? When AI absorbs the entry-level tasks once used to build foundational ability, the result is not always progress. Real competence is built through repetition, friction, and procedural engagement. This article explores how overreliance on AI can create an illusion of mastery, weaken human-in-the-loop judgment, and quietly dismantle the skills ladder that supports professional growth.

Is AI Infrastructure the New Redlining? Inside Bessemer’s $14.5 Billion Data Center Dilemma?

This learning module explores the intersection of AI infrastructure governance & environmental justice through a deep dive into “Project Marvel”- a proposed $14.5 billion hyperscale data center in Bessemer, Alabama.

By moving beyond simple “legal vs. illegal” framing, the guide provides a rigorous Critical Moral Reasoning framework to evaluate the ethical implications of large-scale technology projects on local communities.

Inside the Module

Defining Digital Redlining: Learn how the siting of resource-intensive data centers can mirror historic patterns of systemic exclusion and environmental burden in marginalized communities.

The “Project Marvel” Case Study: Analyze the specific impacts of an 18-building, 700+ acre campus on residents, local ecology (including Rock Mountain Lake), and city zoning.

Competing Ethical Lenses:

Rawlsian Justice: Use the “Veil of Ignorance” to design fair principles for participation and the distribution of burdens.

Libertarian Ethics: Apply principles of self-ownership and property rights to evaluate government overreach and negative duties.

Evidence-Based Reasoning: Move past “facts/values confusion” by separating empirical data from moral justification to build a disciplined ethical argument.

Learning Outcomes

You will learn to construct a formal moral argument—bridging the gap between technical infrastructure and human rights—while identifying the specific evidence needed to prove or refute claims of structural inequity.

The Holcombe Ethics Framework Suite (Overview)

Understand the Holcombe Ethics Framework with this detailed 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.