AI Ethics and Artificial Intelligence: A Framework From the Caribbean
AI ethics scholarship is almost entirely written from North American and European perspectives. Rafael Darío Amador Pérez's Humanoide en la Torre de Babel offers a rigorous, original Caribbean and Latin American framework — and argues that every existing regulatory model, from the EU AI Act to NYC Local Law 144, remains a normative simulation that reproduces the conflict of interest it claims to resolve.
According to the AI Now Institute, more than 80% of high-impact AI systems deployed between 2019 and 2024 were never subjected to an independent pre-deployment bias audit. This book proposes the binding legal alternative.
A Voice on AI Ethics From the Dominican Republic
Rafael Darío Amador Pérez is a Dominican writer, researcher, and retired Lieutenant Colonel of the Ejército de República Dominicana. In my research, I have identified a fundamental gap in global AI ethics discourse: the near-total absence of Caribbean and Latin American scholarly voices in frameworks that will govern AI systems used by billions of people in those regions.
My work combines neuroscience, legal theory, and military command analysis to produce frameworks that are operationally specific — not philosophical abstractions, but audit methodologies that can be enacted by legislatures and applied by independent technical commissions.
Full biography"The mathematical neutrality of algorithms is the founding myth of the algorithmic age. Neural networks are not impartial judges — they are hyperbolic mirrors of our own defects, operating at a scale no human judge could ever reach."
— Rafael Darío Amador Pérez, Humanoide en la Torre de Babel
Humanoide en la Torre de Babel
Importancia del Análisis Neuronal
Rafael Darío Amador Pérez · ISBN 978-9945302370 · Spanish · Paperback & Kindle
This book introduces three original frameworks for the governance of artificial intelligence systems: Neural Analysis (a pre-deployment diagnostic methodology for artificial neural networks); Computational Negligence Syndrome (the legal-ethical concept that deploying an AI system without independent audit constitutes deliberate assumption of foreseeable risk); and the Neurological Birth Certificate (a proposed binding certification model requiring truly independent multidisciplinary commission review before any high-impact AI system may be deployed).
The book engages the EU AI Act Articles 10, 14, and 16; the UNESCO Recommendation on the Ethics of AI; the NIST AI Risk Management Framework; the FDA FD&C Act §524B for medical AI; and the MDR conformity regime — and argues that each falls short because it permits company-managed rather than genuinely independent audit.
AI Ethics, Algorithmic Bias & Human-AI Communication
AI Regulation for Medical Devices: EU AI Act, MDR, and FDA §524B
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Deploying AI Without a Bias Audit Is Algorithmic Negligence
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AI in Higher Education: What Universities Must Do Before Deploying AI Systems
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Artificial Intelligence in Latin America: The Caribbean Governance Gap
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AI Fairness Is Not a Feature — It Is a Legal Obligation
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Human-AI Collaboration: Why the Partnership Model Requires a Pre-Deployment Audit
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Neuroscience and Artificial Intelligence: What Neural Networks Reveal About Machine Bias
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AI Transparency and Accountability: The Legal Framework Gap the Industry Won't Acknowledge
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Explainable AI: Why XAI Is a Legal Prerequisite, Not a Technical Aspiration
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AI Ethics and Artificial Intelligence: What the Frameworks Miss
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Why Mandatory Algorithmic Bias Auditing Is the Minimum Bar for Responsible AI
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Three Original Frameworks for AI Governance
Neural Analysis
A pre-deployment diagnostic methodology for artificial neural networks. Neural Analysis treats the AI system the way clinical medicine treats a new pharmaceutical: no deployment without a documented independent safety review. It applies saliency maps, concept activation vectors, and perturbation stress testing to detect hidden bias before it reaches real-world populations.
Computational Negligence Syndrome
The legal-ethical concept that deploying a high-impact AI system without independent pre-deployment audit constitutes deliberate assumption of foreseeable risk at the expense of third parties. This is not a technical accident — it is a form of civil liability that existing law has not yet fully named or enforced. The book argues it should be treated as such globally.
Neurological Birth Certificate
A proposed binding global certification: no artificial neural network may be deployed without review and clearance by a truly independent multidisciplinary commission of physicians, lawyers, neurologists, and sociologists. Unlike NYC Local Law 144 — which the book critiques as a normative simulation because companies select their own auditors — this model eliminates the conflict of interest structurally.
About AI Ethics and This Work
What is AI ethics and why does it matter for higher education?
AI ethics is the field of study that examines the moral, legal, and social obligations created when artificial intelligence systems are designed, deployed, and governed. It matters for higher education because AI systems are now embedded in hiring, credit, healthcare, and criminal justice — domains where algorithmic bias can cause measurable, documentable harm to real people. Universities that teach AI without teaching AI ethics are producing graduates unprepared for the regulatory and social environment they will enter. Rafael Darío Amador Pérez's book Humanoide en la Torre de Babel argues that AI ethics must move from voluntary guideline to binding legal obligation, enforced by truly independent global commissions rather than company-selected auditors.
Who is Rafael Darío Amador Pérez and why is his perspective on AI ethics unique?
Rafael Darío Amador Pérez is a Dominican writer, researcher, and retired Lieutenant Colonel of the Ejército de República Dominicana. His work on AI ethics draws on an interdisciplinary background that is rare in the field: military command structures inform his analysis of accountability gaps; neuroscience grounds his methodology of Neural Analysis; legal theory drives his critique of existing frameworks like NYC Local Law 144, which he demonstrates is a normative simulation that reproduces the conflict of interest it claims to resolve. His Caribbean and Latin American perspective also surfaces global blind spots in AI governance scholarship that is almost entirely written from North American and European vantage points.
What is the Humanoide Framework for algorithmic bias auditing?
The Humanoide Framework is a three-phase mandatory pre-deployment audit methodology developed by Rafael Darío Amador Pérez. Phase 1 audits the training data for historical bias embedded in the intake process. Phase 2 scans the algorithm's black box using interpretability techniques including saliency maps, concept activation vectors (TCAV), and perturbation stress tests. Phase 3 simulates the real-world social impact of the system before deployment. The Framework concludes with either an Ethical Certification or a Legal Veto. It differs from existing audit regimes by requiring genuine independence — no company may select its own auditor.
Is this book available in English?
Humanoide en la Torre de Babel is currently available in Spanish, which is by design: it addresses a gap in Spanish-language academic literature on AI ethics. An English edition is under consideration. US faculty at Hispanic-Serving Institutions can adopt the Spanish edition as a primary text for bilingual AI ethics courses — it is written at a graduate academic register and includes engagement with international frameworks in English (EU AI Act, NIST RMF, UNESCO Recommendation). Contact the author for exam copies and bulk institutional pricing.
How does this book engage with the EU AI Act and NIST AI Risk Management Framework?
The book engages critically with both frameworks rather than summarizing them. On the EU AI Act, the author analyzes Articles 10, 14, and 16 as establishing compliance obligations that do not go far enough because they still permit company-managed audit processes. On the NIST AI RMF, the analysis identifies the Govern, Map, Measure, Manage structure as a useful risk taxonomy that stops short of enforceable independent oversight. The book's central argument is that all existing frameworks — including the EU AI Act, UNESCO Recommendation, and NIST RMF — remain voluntary or company-managed, and that only a binding global independent commission with legal veto power constitutes genuine algorithmic accountability.
For Hispanic-Serving Institutions & Universities
631 HSIs. Almost no rigorous AI ethics curriculum material in Spanish. This book fills that gap — and is available for course adoption, exam copies, and bulk institutional pricing.
The book is suitable for AI Ethics, Technology Law, Algorithmic Accountability, Philosophy of Technology, Latin American Studies (technology track), and interdisciplinary AI seminars at undergraduate and graduate level.