Is human oversight to AI systems still possible?

Abstract

The rapid proliferation of artificial intelligence (AI) systems across diverse domains raises critical questions about the feasibility of meaningful human oversight, particularly in high-stakes domains such as new biotechnology. As AI systems grow increasingly complex, opaque, and autonomous, ensuring responsible use becomes a formidable challenge. During our editorial work for the special issue "Artificial Intelligence for Life Sciences", we placed increasing emphasis on the topic of "human oversight". Consequently, in this editorial we briefly discuss the evolving role of human oversight in AI governance, focusing on the practical, technical, and ethical dimensions of maintaining control. It examines how the complexity of contemporary AI architectures, such as large-scale neural networks and generative AI applications, undermine human understanding and decision-making capabilities. Furthermore, it evaluates emerging approaches-such as explainable AI (XAI), human-in-the-loop systems, and regulatory frameworks-that aim to enable oversight while acknowledging their limitations. Through a comprehensive analysis, the picture emerged while complete oversight may no longer be viable in certain contexts, strategic interventions leveraging human-AI collaboration and trustworthy AI design principles can preserve accountability and safety. The discussion highlights the urgent need for interdisciplinary efforts to rethink oversight mechanisms in an era where AI may outpace human comprehension.