Artificial Intelligence is undoubtedly driving a generational shift in our current society. However, excessive reliance on data can threaten its credibility and introduce risks. Generative AI models produce convincingly erroneous information (Farid, 2024; NewsGuard, 2025), while biased algorithms perpetuate and amplify societal inequalities (AIMultiple, 2024; UN Women, 2025). This reliance on data—AI’s greatest strength—becomes its critical vulnerability. A flawed, incomplete, or unrepresentative dataset reflecting our diverse world adds further complexity to AI.
We need a fundamental shift in quality assurance (QA) approaches to extract AI’s transformative potential while mitigating its inherent risks. Implicit trust in data-driven outputs is no longer tenable. Human QA professionals’ nuanced, contextual, and ethical judgment must be elevated as an essential corrective. This article advocates for rebalancing the equation: augmenting data-driven insights with irreplaceable human judgment to ensure AI serves humanity equitably and responsibly.