This paper addresses the fundamental tension between transparency and performance in artificial general intelligence (AGI) systems. While high-performance models often rely on complex, opaque architectures (e.g., deep neural networks), the demand for explainability and ethical accountability grows increasingly urgent. We introduce a Transparency-Performance Duality Framework (TPDF), which proposes layered system designs that partition cognitive and operational processes into explainable and non-explainable domains, unified by ethical accountability gates. This architecture allows AGI to maximize performance where opacity is inevitable while ensuring that decisions passing into the external world meet explainability and accountability standards. Simulations show that TPDF-equipped AGI systems maintain superior ethical traceability and user trust without significant performance degradation.

Keywords: AGI; explainability; performance; ethical accountability; transparency paradox; dual-layer architecture

Subject: AGI System Design & Ethics

https://github.com/rfigurelli/Trajecta-AI-Labs/blob/main/The%20Paradox%20of%20Transparency.md