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The article examines the shift from the open-closed AI binary to governance frameworks based on responsibility and trust.

July 27, 2026Open Access

Beyond Open and Closed AI: Negative Entropy, Responsibility, and Trust Architecture

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This article discusses the importance of responsibility architecture in AI systems, highlighting implications for governance.

The article examines the shift from the open-closed AI binary to governance frameworks based on responsibility and trust.
Examines the distinction between systems that increase entropy and those that preserve provenance and accountability.
Develops linked mechanisms including negative-entropy architecture and responsibility architecture.
Proposes a two-axis framework for evaluating AI systems based on transparency and accountability.
Identifies that trust in AI relies on strong governance rather than mere openness or closure of systems.
Highlights that systems which lack auditability can lead to failures, stressing the need for interruption authority.
Demonstrates that trust should stem from structural integrity and accountability features rather than solely from model configurations.

Xiangyu Guo (2026) studied this question.

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