Decoding Black Box AI: The Global Push for Explainability and Transparency

Mazrouei, N. S. A. (2025, October 27). Decoding black box ai: The global push for explainability and transparency. TRENDS Research & Advisory. https://trendsresearch.org/insight/decoding-black-box-ai-the-global-push-for-explainability-and-transparency/

This piece follows the EU’s AI Act and other legislation like it in the pursuit of making AI explainable to ensure that any deployments of it are as transparent as possible. This arose in earlier discussions we had this semester, and seeing it mentioned again in a more focused piece is helpful to understand that potential benefits and drawbacks to this methodology. Mazrouei remarks that while these forms of regulations can ‘guide standardization’, there needs to be an equal push for a standardized definition of what it means for an AI system to be transparent, explainable, and so on. Without such agreed upon metrics, legislation can only do so much to move us towards a decoded AI ecosystem.

Mazrouei notes that in practice, these attempts at standardization are often highly technical. While understanding a standard concept of the technicalities of black box and opaque AI systems, this doesn’t explain the standards and how they would interact within ethical or social dimensions, making such efforts fall short of truly intelligibility.