Artificial Intelligence (AI) has become a pivotal tool for knowledge dissemination across diverse domains. However, existing AI models often reflect biases rooted in Western-centric data, leading to a lack of inclusivity, transparency, and equitable representation. This gap highlights the urgent need for AI systems that can address cultural disparities and promote fairness. Motivated by the challenges of biased knowledge sharing and limited user agency, we propose a conceptual agent-based AI framework designed to enhance fairness, explainability, and participatory governance. Our multi-agent system incorporates specialized agents for bias detection, user feedback integration, and transparency enhancement. The key contributions of this work include a novel framework for dynamic bias mitigation, mechanisms for continuous user-driven model improvement, and strategies to foster trust in AI systems. Experimental results demonstrate improved cultural inclusivity, enhanced model transparency, and increased user trust in AI-driven knowledge dissemination. The implications of this framework are far-reaching, offering a pathway toward more equitable AI adoption, especially in underrepresented and marginalized communities.