Building the Foundation of a Responsible AI

Abstract

The article presents a comprehensive analysis of 15 key principles essential for implementing responsible artificial intelligence (AI) and proposes a practical framework for their evaluation; this includes the three core principles: Explainability, interpretability and ethics. As AI systems become increasingly integrated into society, ensuring their responsible development and deployment has become crucial. The article examines fundamental concepts, including explainability, interpretability, ethics, reliability, safety, robustness, privacy, security, fairness and non-discrimination, human-centric values, inclusive and sustainable innovation, accountability, explainability, transparency, trustworthiness and counterfactual explanation. For each principle, the article provides precise definitions, identifies relevant stages in the AI lifecycle for investigation, and outlines specific metrics and tools for measurement. Building on this analysis, the research introduces a structured evaluation framework featuring a detailed questionnaire that organisations can use to assess their AI systems. The assessment utilises a 1–5 rating scale for each concept, where 1 represents ‘not implemented’ and 5 indicates ‘fully implemented and optimised’. The article also proposes a weighted scoring system that can be customised based on specific use cases or industry requirements. For instance, healthcare AI applications might weigh safety and privacy more heavily, while AI systems that are used in hiring and recruitment might emphasise fairness and non-discrimination. This adaptability ensures the framework’s relevance across different domains and applications. The framework presented serves as a foundation for incorporating and assessing key principles of responsible AI and developing standards in the rapidly evolving field of AI.

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