Demystifying The Black Box_ Responsible AI Governance Without The Jargon
Year: 2025
Type: article
Abstract: This article provides a practical, jargon-free guide to understanding AI governance and the "black box" problem in artificial intelligence. Written by Akintunde Akinbo, it explains how modern AI systems—particularly those based on machine learning—often make decisions in ways that are difficult to understand, even for their creators. The article demystifies the risks associated with opaque AI systems, including hidden bias, lack of accountability, potential misuse, and erosion of public trust. It makes the case for responsible AI governance as a necessary framework to ensure transparency, accountability, fairness, privacy protection, and safety throughout an AI system's lifecycle. Rather than requiring technical expertise, the article focuses on accessible principles that help transform opaque "black box" systems into trustworthy tools that people can understand and rely upon. It emphasizes that governance isn't about slowing innovation, but about ensuring technological progress doesn't come at the expense of fairness, safety, or human rights.
Author: Akinbo, Akintunde
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7127601202
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