TRENDS Research & Advisory has released a new research study – Decoding Black Box AI: The Global Push for Explainability and Transparency – authored by researcher Noor Al-Mazrouei, Director of AI and Technology Department at TRENDS. The study addresses one of the most pressing contemporary issues in the field of artificial intelligence (AI): transparency and explainability in complex models commonly referred to as “black boxes,” whose decision-making mechanisms are difficult to understand.

Al-Mazrouei emphasized that the growing use of AI in sensitive domains such as healthcare and finance has intensified calls from regulatory bodies and societies to understand how these systems operate. This has sparked a global movement to establish clear regulatory frameworks that ensure explainability and accountability.

The study highlights that the European Union is leading global regulatory efforts through the EU Artificial Intelligence Act, the first comprehensive legal framework requiring that intelligent systems provide understandable explanations for their decisions. It also notes that several countries have begun integrating principles of transparency and explainability into their national AI strategies, though the level of commitment and approach varies from one country to another.

Furthermore, the study sheds light on the challenges global institutions face in implementing Explainable Artificial Intelligence (XAI), particularly as deep learning models become more complex. Often, achieving higher predictive accuracy comes at the expense of transparency and interpretability.

The study also explores key technical approaches to enhancing transparency, such as hybrid models that combine interpretable systems with complex models, and visual explanation tools like Grad-CAM, which highlight to users the areas of data most influential in AI decision-making. It also discusses the development of interactive interfaces designed to make AI outputs more understandable to end users.

The study stresses that promoting transparency goes beyond the technical dimension – it requires cross-sector institutional collaboration and the establishment of shared international standards to ensure consistency in governance and adherence to ethical values. It commends the role of international organizations such as ISO and IEEE in unifying efforts and developing standardized frameworks that foster trust among developers, users, and decision-makers.

The study concludes by emphasizing that the path toward responsible and trustworthy AI requires a balance between performance and transparency, as well as the integration of technical, regulatory, and ethical dimensions. It calls for greater international cooperation and research to develop practical, applicable solutions that keep pace with the rapid evolution of this vital field.