Published October 28, 2025 | Version Version 1
Preprint Open

Why Stateful AI Fails Without Ethical Guardrails: Real Implementation Challenges and the De-Risking Architecture

Creators

  • 1. Antiparty Press

Description

Stateful AI systems that remember users create three architectural failure modes: persistence exploitation, data asymmetry extraction, and identity capture. Current regulatory frameworks mandate disclosure but not safeguards, enabling documented non-autonomy rather than actual consent.

This paper proposes a five-principle de-risking architecture: architectural consent (cryptographic enforcement), user-controlled visibility and modification rights, temporal data decay, manipulation detection with hard stops, and independent audit trails. The framework addresses why ethical guardrails are economically deprioritized (10x engineering cost, 90% monetization reduction) and why de-risking is becoming mandatory under tightening regulation.

The regulatory and market window for voluntary de-risking closes within 18 months. Companies building this architecture now will lead 2027+ markets; companies retrofitting later will face exponentially higher costs.

For builders, users, organizations, regulators, investors, and policymakers responsible for AI systems.

Keywords: algorithmic exploitation, AI governance, user autonomy, privacy-preserving AI, ethical guardrails, personalization, consent architecture, digital rights

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Additional details

Dates

Created
2025-10-31
Date of initial publication and version release for this preprint

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