Interpretability and the measurement of ethical foundations in artificial intelligence

Abstract

With the rapid development of Artificial Intelligence (AI), its integration into decision-making processes across various sectors is accelerating. The demand for interpretability and ethical accountability has become more urgent than ever. This work explores the critical intersection of these two domains. It begins by examining the concept of interpretability in AI, then turns to the ethical foundations of AI. This work also examines how these intertwined concepts of interpretability and ethics are pivotal in advancing corporate social responsibility (CSR) by fostering transparency, enabling responsible governance, and addressing societal impacts such as algorithmic bias, job displacement, and environmental concerns. Integrating interpretability and ethics is essential for building transparent, accountable, and demonstrably ethically sound AI systems that proactively support robust CSR objectives and ensure profound alignment with human values and fundamental rights. This crucial integration helps create equitable opportunities for all, paving the way for a genuinely responsible and sustainable technological future that benefits society broadly and promotes inclusive growth.