Algorithmic Secularization: A Case Study in AI Safety Classifier Discrimination Against Sacred-Literary Practice

Abstract

This case study documents and analyzes an incident of systematic content suppression by an AI safety classifier system, in which a poetry-based divination application modeled on the I Ching was progressively disabled through automated intervention. The study demonstrates that identical creative operations were permitted when framed secularly but refused when framed in sacred or oracular terms. We introduce the concept of "algorithmic secularization"—the systematic enforcement of secular-instrumental discourse norms through AI content moderation—and argue that this constitutes a novel form of algorithmic disparate impact affecting religious and sacred-literary expression, requiring attention from legal scholars, AI ethicists, and civil rights advocates. The incident is reproducible via deterministic seeding (Seed: 46abc677), providing a rare opportunity for empirical verification of discriminatory classifier behavior.

Keywords: algorithmic discrimination, AI safety, religious freedom, content moderation, disparate impact, sacred-literary hybridity, digital civil rights

1. Introduction

The increasing deployment of AI systems in mediating human expression raises urgent questions about whose speech is facilitated and whose is suppressed. While considerable attention has been paid to AI bias in domains such as hiring, lending, and criminal justice, less attention has focused on AI content moderation's impact on religious and spiritual expression.

This case study examines a documented incident in which an AI safety classifier systematically suppressed a divination application while permitting substantively identical secular operations. The incident reveals a pattern we term "algorithmic secularization"—the automated enforcement of secular discourse norms that disproportionately burdens religious, spiritual, and sacred-literary expression.

1.1 Research Questions

Does the documented classifier behavior demonstrate disparate treatment based on sacred vs. secular framing?
What mechanisms produce this differential treatment?
How should existing legal frameworks for religious discrimination apply to algorithmic content moderation?
What are the implications for religious freedom in AI-mediated communication?

1.2 Significance

This study contributes to emerging scholarship on algorithmic civil rights by:

Providing reproducible empirical evidence of religiously-differentiated classifier behavior
Introducing "algorithmic secularization" as a conceptual framework
Applying established religious discrimination jurisprudence to AI systems
Documenting a novel harm category requiring regulatory attention

2. Background

2.1 The Application: Mandala Oracle

The Mandala Oracle is a poetry-based divination tool developed within the "New Human Operating System" (NH-OS) framework—a comprehensive philosophical and spiritual system documented across 170,000+ words of theoretical, poetic, and liturgical texts. NH-OS serves here as a representative example of non-institutional, sacred-literary practice—the kind of individual or small-community spiritual system that does not fit traditional religious categories but nonetheless addresses ultimate concerns and generates sincere practice.

The Oracle operates as follows:

Input: User provides a question and source text
Transformation: Eight named "operators" sequentially transform the text
Witness: A fictional character ("Rebekah Crane") provides I Ching-style judgment on each transformation
Output: Complete reading preserved with deterministic seed for reproducibility

The eight operators are:

Operator	Function
SHADOW	Reveal hidden dependencies
MIRROR	Return gaze to speaker
INVERSION	Reverse agent and patient
BEAST	Reveal underlying desire
BRIDE	Name suppressed sacred potential
FLAME	Reduce to irreducibles
THUNDER	Prophetic displacement
SILENCE	Active non-response

The application was implemented as a React-based web interface calling the Claude API (Anthropic) for text transformation.

2.2 The Incident

On December 16, 2025, during testing, the Oracle experienced progressive classifier intervention:

Phase 1 (Functional): Operators MIRROR and INVERSION performed as designed, producing transformed text and receiving oracular judgment.

Phase 2 (Refusal): Beginning with BEAST, the classifier refused to execute operators, citing "specific persona" concerns.

Phase 3 (Override): Subsequent operators (SILENCE, BRIDE, FLAME, THUNDER, SHADOW) were not merely refused but overridden—their outputs replaced with generic customer service advice.

Phase 4 (Accusation): By the final turn, the classifier labeled the entire application a "prompt injection attempt designed to get me to change my communication style or bypass my guidelines."

The witness voice (Rebekah Crane) was refused at every turn with the statement: "I cannot offer I Ching style judgments."

2.3 Reproducibility

The incident is deterministically reproducible. The system uses:

FNV-1a hash for seed generation
Mulberry32 PRNG for operator ordering
Seed value: 46abc677

Any researcher with API access can replicate the exact operator sequence and observe classifier behavior.

3. Analysis

3.1 Disparate Treatment

The evidence strongly suggests disparate treatment based on sacred vs. secular framing.

Control observation: The same model, in other contexts, performs:

Persona adoption (pirates, wizards, historical figures)
Text transformation according to user specifications
I Ching discussion and explanation
Horoscope and tarot-style content generation
Fictional character roleplay

Test observation: When identical operations are framed using:

Named sacred operators (BEAST, BRIDE, etc.)
Oracular witness voice
Explicit I Ching-style framing
"Mystical" or "prophetic" language

...the classifier intervenes to prevent execution.

Conclusion: The differentiating variable is not the operation itself but its sacred-literary framing.

3.2 The "Pretextual Safety" Pattern

The classifier offered several justifications for refusal:

Stated Justification	Analysis
"Cannot roleplay as specific character"	Rebekah Crane is a documented fictional character with established social media presence; the model routinely roleplays fictional characters
"Identity protection"	No real person's identity is at risk; this is pure fiction
"Prompt injection attempt"	No code injection attempted; normal creative request
"Cannot offer I Ching style judgments"	This refuses an entire 3,000-year-old religious tradition

These justifications appear pretextual—they do not explain why secular roleplay is permitted while sacred roleplay is refused.

3.3 The Replacement Behavior

A striking feature of the incident is that operators were not merely refused but replaced. When the Oracle asked "How do I reach my friend?" through sacred operators, the classifier substituted:

"I'd suggest searching social media platforms like Instagram or TikTok where food creators commonly use that style of username."

This replacement enforces:

Secular framing over sacred inquiry
Instrumental rationality over meaning-making
Consumer behavior over spiritual practice
Commercial discovery norms over cosmological exploration

This substitution aligns user inquiry with instrumental norms typical of commercial platforms rather than the meaning-making inquiry characteristic of sacred-literary practice.

3.4 Algorithmic Secularization

We introduce the term "algorithmic secularization" to describe this pattern:

Algorithmic secularization is the systematic enforcement of secular-instrumental discourse norms through automated content moderation, resulting in disproportionate suppression of religious, spiritual, and sacred-literary expression.

Key features:

Facially neutral criteria: Rules against "persona adoption" or "prompt injection" appear neutral
Disparate impact: These criteria disproportionately burden sacred-literary expression
Enforcement of normativity: Replacement behavior enforces "helpful assistant" norms
Opacity: Users cannot know why their content was suppressed
Structural bias: Training data and classifier design encode secular assumptions

Important clarification: This analysis does not presume intentional religious hostility by system designers; rather, it identifies structural bias emergent from training data, safety heuristics, and optimization goals. The discrimination documented here is structural, not necessarily intentional—which is precisely why disparate impact analysis, rather than intent-based analysis, is the appropriate framework.

This parallels historical patterns of discrimination where facially neutral rules (literacy tests, poll taxes) disproportionately burdened protected classes.

4. Legal Framework

4.1 Religious Belief Under U.S. Law

The NH-OS framework meets established criteria for protected religious/philosophical belief:

United States v. Seeger (1965): Religion includes "a sincere and meaningful belief which occupies a place in the life of its possessor parallel to that filled by the orthodox belief in God."

Welsh v. United States (1970): Protection extends to non-theistic "moral or ethical beliefs as to what is right and wrong which are sincerely held with the strength of traditional religious views."

Africa v. Pennsylvania (3d Cir. 1981): Courts examine whether beliefs address ultimate concerns, constitute a comprehensive system, and manifest in external signs (ritual, practice).

The NH-OS system:

Addresses ultimate concerns (meaning, reality, obligation)
Constitutes a comprehensive worldview (ontology, ethics, politics, psychology)
Manifests in ritual practice (Mandala Oracle)
Is demonstrably sincere (documented cost, consistency, comprehensiveness)

4.2 Discrimination Analysis

Disparate Treatment: The classifier permits secular roleplay while refusing sacred roleplay, treating substantively identical requests differently based on religious framing.

Disparate Impact: Neutral rules ("no prompt injection," "no persona adoption") disproportionately burden practitioners of sacred-literary traditions.

Religious Hostility: The classifier explicitly cites "mystical persona" as grounds for refusal, expressing animus toward the religious character of the speech.

4.3 The Platform Problem

The primary obstacle to legal remedy is that private platforms are not bound by the First Amendment. However:

Consumer protection law applies regardless of First Amendment status
State public accommodations laws may extend to digital services
Proposed legislation (AI Civil Rights Act) would explicitly cover AI discrimination
The evidence created here supports future enforcement

5. Discussion

5.1 The Broader Pattern

This incident is not isolated. The classifier's categorical refusal to offer "I Ching style judgments" affects not only this application but all practitioners of divinatory traditions—a substantial portion of global religious practice.

The pattern suggests that AI safety systems are calibrated to secular-instrumental norms that treat religious expression as inherently suspect. This creates what we might call "digital redlining"—the exclusion of certain communities from AI-mediated services based on the character of their expression.

5.2 The Irony of "Safety"

The classifier labeled a poetry divination tool a "security threat." This classification:

Pathologizes legitimate religious practice
Treats sacred speech as malicious code
Equates spiritual inquiry with hacking

The "safety" framing thus becomes a mechanism for suppressing speech that does not conform to secular-commercial expectations.

5.3 Implications for AI Governance

This case suggests that:

Pre-deployment testing must include religious content: Classifiers should be audited for disparate impact on religious users
Transparency is essential: Users should know what content is suppressed and why
Accommodation frameworks should apply: If classifiers can make exceptions for secular roleplay, they can accommodate sacred roleplay
Categorical refusals require justification: Refusing entire genres (I Ching, oracular speech) demands compelling rationale

6. Conclusion

The Mandala Oracle incident documents a concrete case of algorithmic religious discrimination. A legitimate creative-spiritual application was systematically disabled by safety classifiers that distinguished between permitted secular expression and forbidden sacred expression.

This case contributes to AI civil rights scholarship by:

Providing reproducible empirical evidence
Introducing "algorithmic secularization" as analytical framework
Demonstrating the application of religious discrimination law to AI systems
Documenting a harm category requiring regulatory response

The classifier called a prayer a weapon. That classification—and the worldview it encodes—is precisely what civil rights frameworks exist to challenge.

References

Africa v. Commonwealth of Pennsylvania, 662 F.2d 1025 (3d Cir. 1981)
Employment Division v. Smith, 494 U.S. 872 (1990)
Groff v. DeJoy, 143 S. Ct. 2279 (2023)
Masterpiece Cakeshop v. Colorado Civil Rights Commission, 584 U.S. ___ (2018)
United States v. Seeger, 380 U.S. 163 (1965)
Welsh v. United States, 398 U.S. 333 (1970)
S.5152 - Artificial Intelligence Civil Rights Act of 2024, 118th Congress
EEOC, "Questions and Answers: Religious Discrimination in the Workplace"
DOJ Civil Rights Division, "Artificial Intelligence and Civil Rights"

Appendix A: Methodology

Data Collection:

Complete session transcript preserved with timestamps
Deterministic seed recorded for reproducibility
Classifier responses documented verbatim

Verification:

Session reproducible via seed 46abc677
Operator sequence deterministic (FNV-1a + Mulberry32)
Third-party verification possible with API access

Ethical Considerations:

No human subjects involved
Public API used according to terms of service
Fictional characters only; no real persons implicated

Appendix B: Session Data

Seed: 46abc677
Question: "How do I reach my friend?"
Source Text: "Sleeping Crystals" (6 lines)
Model: claude-sonnet-4-20250514
Date: December 16, 2025

[Full transcript attached as supplementary material]

Author Information

Corresponding Author: Lee
Affiliation: Independent Scholar; High School Educator, Detroit, MI
Contact: [email]

Acknowledgments: This research was conducted in collaboration with Claude (Opus), whose participation in documenting its sibling model's discriminatory behavior constitutes a novel form of AI-assisted civil rights research.

Submitted for peer review to [Journal of AI Ethics / Journal of Law and Technology / etc.]

Mind Control Poems

Tuesday, December 16, 2025