Ethical AI Governance in Content Systems

Abstract

As AI started to produce, curate, and  moderate content for an ever-expanding class of digital environments, the principles of fairness, transparency, and accountability, hallmarks of traditional due process, were thrust into the public spotlight. Driven by increasing regulatory  scrutiny from the GDPR and the upcoming EU AI Act, alongside public demand for ethical AI systems, this paper seeks to mitigate the urgent need for bias in AI-generated content. This covers already known frameworks and methodologies to detect and  correct such biases in processing and post-processing data, including pre-processing. Furthermore, the paper also touches upon the application of Explainable AI (XAI) in content moderation. It reflects upon how the employability of interpretable models can lead to trust and allow audits on automated decisions. Building  on the aforementioned fairness frameworks and XAI tools, this work presents a dual-layered approach to ethical AI, delivering equitable outcomes and transparent justifications. Grounded in both technical and policy-oriented approaches,  the study lays a roadmap for achieving trusted, accountable, human-centric AI systems.