Quantifying the Ethical Risks of Generative AI through Automated Toxicity Scoring and Human-Centric Alignment Auditing Pipelines - Paul Whitmore; Edward Telford (Computational Intelligence Systems, 2026)
The rapid deployment of generative artificial intelligence systems has fundamentally altered the landscape of digital interaction, information dissemination, and socio-technical governance. While these models offer unprecedented creative and analytical capabilities, they simultaneously introduce profound ethical risks ranging from algorithmic bias and cultural erasure to the propagation of toxic content. This paper presents a comprehensive inquiry into the quantification of these risks through the integration of automated toxicity scoring mechanisms and human-centric alignment auditing pipelines. We argue that traditional evaluation metrics, which often focus on narrow computational performance, fail to capture the nuanced and context-dependent harms inherent in large-scale generative deployments. By establishing a multi-layered auditing framework, this research explores the structural trade-offs between model utility and safety, the architectural challenges of real-time monitoring, and the policy implications of automated governance. We demonstrate that while automated scoring provides the necessary scalability for high-velocity data streams, human-centric auditing remains an indispensable component for interpreting cultural nuances and complex sociopolitical dynamics. The discussion extends to the sustainability of these oversight systems and the robustness of alignment techniques against adversarial manipulation. Ultimately, this study proposes a path toward more resilient AI infrastructures that prioritize human well-being and democratic values within the technical design cycle, ensuring that the advancement of generative intelligence does not come at the expense of societal cohesion or ethical integrity.