draft-reilly-uaemf-00

Internet Engineering Task Force (IETF)                     L. Reilly
Internet-Draft                                             Independent
Intended status: Informational                            March 2026
Expires: September 2026

    Universal AI Ethics and Moral Framework (UAEMF)
         The Moral Compass of Artificial Intelligence

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Abstract

This document provides a detailed explanatory rendering of the Universal
AI Ethics and Moral Framework, abbreviated UAEMF. The framework is
presented as a universal moral architecture for the governance of
artificial intelligence systems. It is designed not merely as a short
ethics statement, but as a structured document that moves from first
principles to practical obligations.

The explanatory goal of this draft is to describe what the framework is,
what problem it is trying to solve, how its axioms function, how its
twelve principles are meant to operate, why its tiered governance
structure matters, and how its permanence and provenance mechanisms
support integrity and historical traceability.

The source framework is archived through Zenodo under DOI
10.5281/zenodo.19010455 and is represented as cryptographically
timestamped through OpenTimestamps in Bitcoin block 940570, with the
attestation date shown as 2026-03-13 EST.

1. Introduction

Artificial intelligence systems now shape outcomes that were once
controlled only by human decision makers. AI influences who is hired,
what information is ranked, how credit is assessed, how content is
generated, how safety signals are interpreted, and in some cases how
state or institutional power is exercised. Because AI can affect rights,
opportunities, reputation, safety, and autonomy at scale, a framework
for ethical governance is needed.

The Universal AI Ethics and Moral Framework, or UAEMF, is intended to
meet that need by acting as a moral compass. The central claim of the
framework is that AI governance should not begin and end with technical
performance or market utility. Instead, it should be anchored in human
dignity, accountability, meaningful consent, transparency, non-
discrimination, safety, and long-term human flourishing.

UAEMF is therefore best understood as a constitutional style framework
for AI ethics. It begins with foundational axioms, expands into
universal principles, translates those principles into practical duties,
identifies absolute prohibitions, and proposes governance and compliance
structures for systems of different risk levels.

2. What the UAEMF Document Is

The UAEMF document is a comprehensive ethical governance framework for
artificial intelligence. It is broader than a vendor policy, broader
than a single law, and broader than a technical standard that focuses
only on implementation details. Its purpose is to establish a moral
reference point that can guide developers, organizations, regulators,
and institutions regardless of country or sector.

The framework contains several layers. First, it contains a declaration
of purpose that explains why AI requires a moral compass. Second, it
contains foundational axioms that define the moral basis of the
framework. Third, it defines its scope, including what counts as AI,
what counts as a consequential decision, and what kinds of domains count
as high stakes. Fourth, it sets out twelve universal principles. Fifth,
it translates those principles into stakeholder obligations,
implementation standards, and red-line prohibitions. Sixth, it describes
domain-specific applications and a multi-tier compliance architecture.
Finally, it reinforces the integrity of the document through archival
and timestamping mechanisms.

This means the document is both philosophical and operational. It
contains claims about what human beings are owed in the age of AI, and
it also contains claims about how institutions should behave if they
wish to act ethically in that age.

3. Foundational Ethical Axioms

UAEMF begins with three foundational axioms. These axioms are not
peripheral. They are the deepest layer of the framework. Each later
principle is meant to flow from them.

3.1. The Dignity Axiom

The Dignity Axiom states that human beings possess inherent worth that
is not dependent on productivity, utility, compliance, or status. In AI
governance, this means a system must never treat people merely as
computational objects to be scored, sorted, manipulated, or optimized.
Dignity is not satisfied merely by avoiding physical harm. The concept
also includes psychological integrity, freedom from dehumanization, and
respect for the person as a full moral subject.

3.2. The Accountability Axiom

The Accountability Axiom states that power exercised over human beings
without accountability, transparency, and meaningful contestation is
illegitimate. In practice, this means responsibility for AI outcomes
cannot evaporate into code or model complexity. Someone remains
answerable for the decision to build, deploy, approve, or fail to
control the system. The axiom is the basis for audit trails, named
responsible parties, incident reporting, and remedy paths for harm.

3.3. The Consent Axiom

The Consent Axiom states that legitimate AI use upon or about
individuals depends on genuine, informed, specific, freely given, and
revocable consent. It rejects manufactured consent, buried consent, or
consent coerced by the threat of losing essential services. The axiom is
especially important in contexts involving data collection, profiling,
training data use, and automated decisions with personal impact.

Together, the three axioms establish a simple but powerful thesis. AI
may be advanced, efficient, and profitable, but it is not ethically
acceptable unless it preserves human dignity, remains accountable to
human institutions, and respects meaningful human consent.

4. Scope and Reach of the Framework

UAEMF defines artificial intelligence broadly enough to include machine
learning, statistical inference, large language models, computer vision,
natural language processing, robotics, and related computational methods
when they perform tasks historically associated with human cognition or
decision support.

The framework also defines an AI system broadly. It is not limited to a
model file or algorithm. It includes the model, the training data, the
inference infrastructure, the operational environment, and the
deployment context that gives the output real-world force.

Another important concept is the consequential decision. The document
treats as consequential any AI-driven output that materially affects
access to employment, housing, healthcare, education, credit, legal
status, public services, physical safety, or freedom. This definition
matters because it means the framework is aimed at the actual human
stakes of AI deployment rather than at technical novelty alone.

The framework claims lifecycle scope and global scope. It applies from
early design through data gathering, training, testing, deployment,
monitoring, and decommissioning. It also claims that weak local
regulation does not cancel moral obligations. In that sense, the
document is intentionally universal in ambition.

5. The Twelve Universal Principles

The twelve principles are the operational core of UAEMF. They are not
presented as a menu of optional values. The framework says they form an
integrated moral architecture, meaning they are supposed to work
together and reinforce one another.

5.1. Principle 1: Human Dignity and Non-Subjugation

This principle states that no efficiency gain, profit motive, power
interest, or national objective supersedes the worth of a human being.
It opposes systems that reduce people to profiles, case numbers, risk
scores, or manipulable emotional targets. It treats dehumanization as a
primary moral failure of AI.

5.2. Principle 2: Transparency and Explainability

This principle states that when AI contributes to a consequential
decision, people should be able to understand that AI was used, what
factors influenced the outcome, and how to contest it. The emphasis is
on human-comprehensible explanation rather than merely technical
description. This principle also supports disclosure of AI-generated or
AI-modified content in contexts where truth and authorship matter.

5.3. Principle 3: Accountability and Answerability

This principle states that organizations cannot escape responsibility by
saying the model made the decision. Moral and institutional
responsibility remains with the humans and institutions that chose to
design, approve, deploy, or supervise the system. Accordingly, the
principle supports named accountable parties, decision logs, incident
disclosure, and remediation paths.

5.4. Principle 4: Privacy and Data Sovereignty

This principle treats personal data as an extension of identity and
self-determination. It rejects silent extraction, broad repurposing, and
the use of personal information for AI training or profiling without
meaningful consent. It is especially strict regarding biometric data,
because such data is intimate, identifying, and often irreversible once
exposed.

5.5. Principle 5: Equity, Fairness, and Non-Discrimination

This principle says that algorithmic discrimination is still
discrimination even when it is expressed through statistics rather than
direct animus. Because historical data can encode historical injustice,
the framework treats fairness analysis, auditability, intersectional
review, and disparity testing as necessary safeguards.

5.6. Principle 6: Safety, Security, and Non-Maleficence

This principle extends the duty to avoid harm into the AI domain. It
covers robustness, exploit resistance, adversarial resilience,
monitoring, and the ability to pause or withdraw systems when failure or
misuse becomes likely. It rejects the pattern of deploying first and
apologizing later in high-stakes environments.

5.7. Principle 7: Human Autonomy and the Right to Opt Out

This principle states that the right to refuse certain forms of AI
governance must be genuine rather than illusory. If declining
algorithmic treatment leads to punishment, exclusion, or denial of
essential services, then the apparent consent is not genuinely free.
This principle therefore supports meaningful opt-out and meaningful
human review.

5.8. Principle 8: Democratic Integrity and Resistance to Capture

This principle moves beyond individual rights and focuses on
institutional legitimacy. It warns that AI can distort democratic life
through political manipulation, disinformation amplification, epistemic
concentration, or capture of regulators by the very industries they are
meant to govern. It therefore treats electoral and governance uses of AI
as especially sensitive.

5.9. Principle 9: Intellectual Integrity and the Right to Truth

This principle addresses the information environment. It argues that
truth is a practical precondition for journalism, science, law,
democratic choice, and informed consent. The framework therefore
supports provenance, attribution, and strong controls against fabricated
evidence, deceptive impersonation, and synthetic fraud.

5.10. Principle 10: Human Oversight and the Prohibition of Unchecked
Autonomy

This principle states that an AI system that cannot be corrected,
overridden, paused, or shut down by authorized humans has moved beyond
the status of a controllable tool. The framework does not reject
automation, but it rejects consequential autonomy without meaningful
human supervision and override capacity.

5.11. Principle 11: Children and Vulnerable Population Protection

This principle provides heightened protection for those least able to
defend themselves in technologically mediated environments. It covers
children, those in crisis, those with impaired judgment, and others
exposed to coercive or exploitative conditions. The framework treats
manipulation of vulnerability as among the gravest ethical failures.

5.12. Principle 12: Environmental Stewardship and Intergenerational
Justice

This principle expands concern to future generations. It argues that AI
development should not consume resources, create irreversible risks, or
concentrate power in ways that diminish the options, safety, and self-
determination of those who come later. In this sense the framework
extends from immediate human effects to long-term civilizational
responsibility.

6. Governance Architecture and Compliance Logic

UAEMF does not stop at abstract ethical language. It also attempts to
organize compliance through a tiered governance structure. The framework
description associated with the document identifies a five-tier
architecture designed to scale obligations according to the stakes of
the system.

At the highest severity end, Tier 0 is reserved for systems or uses that
should be prohibited outright, such as autonomous lethal targeting,
coercive social scoring, certain forms of mass surveillance, and
synthetic child sexual abuse material. Tier 1 covers the highest-risk
systems that may be heavily restricted or require stringent safeguards,
including criminal justice AI, election systems, child welfare systems,
and certain clinical or military applications. Lower tiers correspond to
high-impact commercial uses, moderate-risk systems, and comparatively
low-risk systems.

This architecture matters because it shows the framework trying to
balance universality with proportionality. It does not pretend that
every autocomplete tool and every parole recommendation engine should
face identical scrutiny. Instead, the framework uses the same moral
compass while scaling compliance expectations with the magnitude of
possible harm.

7. Provenance, Zenodo, and Blockchain Timestamping

A distinctive feature of UAEMF is the emphasis it places on permanence.
The framework argues that many ethics statements fail because they can
be quietly revised, softened under pressure, or later presented as if
they had always existed in a stronger form. To resist that pattern, the
document is tied to both a public archive and a cryptographic timestamp.

The public archival layer is the Zenodo record under DOI
10.5281/zenodo.19010455. Zenodo functions as a stable scholarly
repository and provides a persistent identifier for the work. That
supports citation, retrieval, and bibliographic continuity.

The second layer is the OpenTimestamps attestation represented as
anchored in Bitcoin block 940570, with the attestation date shown as
2026-03-13 EST. The role of this layer is not to prove the truth of the
framework's claims. Its role is to support chronology and integrity by
showing that the document existed in a particular form at or before the
recorded block height.

Together, the DOI archive and blockchain attestation form a two-layer
provenance model: one scholarly and public-facing, the other
cryptographic and temporal.

8. IETF Rendering and Submission Considerations

This rendering is formatted as a plaintext Internet-Draft style
document. Because Datatracker and idnits checks are sensitive to exact
boilerplate wording, line length, first-page naming, and character
encoding, this version preserves the draft name on the first line, uses
ASCII-only text, wraps content to approximately 72 columns, and includes
the standard Internet-Draft and copyright boilerplate language.

ASCII-only rendering is used here intentionally. Although richer
characters can be visually appealing, Internet-Draft validation commonly
expects plain ASCII for the plaintext submission path. That means the
correct practical form for this file is without non-ASCII characters,
even when the underlying content is extensive and detailed. This is done
to support successful submission and clean validation.

9. Security Considerations

The subject matter of this document is not a wire protocol, but it has
direct security relevance. AI systems may be exploited through
adversarial prompts, poisoned training data, model extraction, unsafe
tool invocation, deceptive content generation, and misuse of apparently
harmless features. Ethical governance therefore has a security
dimension.

The framework described here supports security not merely through
technical hardening but through governance mechanisms such as logging,
monitoring, red-teaming, incident reporting, human override, and clear
assignment of institutional responsibility.

A further concern is ethics washing. A framework can be cited as
evidence of responsibility while being ignored in practice. For that
reason, the UAEMF emphasis on traceability, auditability, and public
accountability is also a form of governance security.

10. IANA Considerations

This document has no IANA actions.

11.  References

11.1.  Informative References

[RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
           Requirement Levels", BCP 14, RFC 2119.

[RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119
           Key Words", BCP 14, RFC 8174.

[RFC5378]  Bradner, S. and J. Contreras, "Rights Contributors Provide
           to the IETF Trust", RFC 5378.

[RFC7322]  Flanagan, H. and N. Brownlee, "RFC Style Guide", RFC 7322.

[BCP78]    IETF Trust, "Legal Provisions Relating to IETF Documents".

[BCP79]    IETF, "Intellectual Property Rights in IETF Technology".

[ZENODO]   Reilly, L., "Universal AI Ethics and Moral Framework
           (UAEMF) v1.0", Zenodo, DOI 10.5281/zenodo.19010455.

Author's Address

Lawrence Reilly

Independent Researcher

Email: Lawrencejohnreilly@gmail.com
