As artificial intelligence (AI) systems increasingly shape societal interactions, ensuring their ethical development has become a global priority. A foundational challenge lies in the data annotation process, where biases, cultural insensitivity, and lack of diversity can propagate into AI models, undermining fairness and trustworthiness. To address this gap, we propose the Ethical Impact Score (EIS), a novel framework for quantifying ethical considerations in datasets used to train AI systems. The EIS integrates key metrics such as offensive content, cultural sensitivity, diversity, and representation, offering a weighted scoring mechanism tailored to specific contexts and regulatory requirements. By embedding ethical accountability into the data annotation stage—the cornerstone of AI development—this framework enables organizations and governments to benchmark, monitor, and improve the ethical quality of AI systems. We demonstrate how EIS can serve as a measurable standard for compliance with emerging AI regulations, such as the EU AI Act, while fostering transparency, inclusivity, and public trust. This work underscores the critical role of structured, quantifiable approaches in advancing ethical AI and sets a foundation for responsible innovation in an era of rapid technological advancement.