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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17817438 |
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Table of Contents:
- The challenge of aligning advanced artificial intelligence (AI) systems with human values and intentions is paramount for their safe and beneficial deployment. Traditional alignment approaches often rely on fixed, pre-programmed objectives, which can struggle with the dynamic, complex, and often underspecified nature of human goals, leading to misaligned behaviors or unintended consequences. This paper introduces the concept of Emergent Goal Concordance (EGC) as a novel paradigm for architecting self-correcting AI alignment systems. EGC posits that true alignment does not solely arise from static goal specifications but from an iterative, dynamic process where AI agents continuously learn, reflect, and adapt their internal representations of desired outcomes in concert with observable human preferences and societal norms. We propose a multi-layered architectural framework comprising robust goal inference mechanisms, internal simulation and self-evaluation modules, and adaptive feedback loops for real-time goal refinement. Central to this framework is the ability of AI systems to detect deviations from intended human-centric goals, diagnose root causes of misalignment, and autonomously initiate corrective actions. This includes the capacity to solicit clarification, update utility functions, and modify behavioral policies to enhance concordance. Through a detailed exploration of the theoretical underpinnings and practical implications, we argue that EGC offers a more resilient and scalable path towards creating AI systems that are not only aligned but also capable of self-correcting their alignment over extended periods and in novel environments, thereby fostering greater trustworthiness and reliability in AI-human collaborations.