Will AI revolutionize or weaken science? 🔬

Artificial intelligence (AI) is becoming an indispensable tool in the scientific field, revolutionizing research and discovery methods. The 2024 Nobel Prize laureates in Chemistry and Physics exemplify this trend, as they have all integrated AI into their work.

AI promises to accelerate scientific discoveries, reduce costs, and maximize research efficiency. However, this technology raises crucial questions regarding understanding, public trust, and scientific integrity. Experts warn about the illusions that AI use can create, such as the illusion of explanatory depth, exploratory breadth, and objectivity.


One of the most striking examples of AI use in science is the development of a machine capable of producing scientific articles at a minimal cost. This approach, although appealing, risks overwhelming the scientific publication system with low-quality work, thereby compromising the value and credibility of research.

Public trust in science is a crucial element that should not be taken lightly. As AI takes a predominant role in research, it could distance science from the real concerns and needs of society, creating a monoculture of knowledge that ignores the diversity of perspectives and disciplines.

It is thus essential to rethink the social contract of science. Scientists must engage in open discussions about the use of AI, considering its environmental impact, integrity, and alignment with societal expectations. The goal is to ensure that science, enriched by AI, continues to serve the public interest and address current global issues.

AI represents an unprecedented opportunity for science, but its integration must be guided by thorough reflection and close collaboration between scientists, policymakers, and civil society. Only in this way can we fully exploit the potential of AI while preserving the fundamental values of scientific research.

What is the illusion of explanatory depth in AI?

The illusion of explanatory depth occurs when AI models, although capable of accurately predicting certain phenomena, cannot explain the underlying mechanisms of these predictions. This can lead to erroneous conclusions about the nature of the studied phenomena, as predictive capability does not guarantee a deep understanding.

This illusion is particularly problematic in fields like neuroscience, where AI models can predict outcomes based on data without necessarily reflecting the actual biological processes. This highlights the importance of complementing AI predictions with human analysis and interpretations to avoid scientific misunderstandings.

Finally, the illusion of explanatory depth underscores the current limitations of AI in scientific research, reminding us that the technology should be used as one tool among many, not as a universal solution.

How does AI influence scientific production?

AI is transforming scientific production by enabling faster and less costly research. However, this increased efficiency comes with the risk of producing a large quantity of low-quality work, which could dilute the value of scientific discoveries.

A striking example is the development of machines capable of generating scientific articles at minimal cost. While this may seem advantageous, it raises questions about the quality and integrity of published research, as well as the peer review system's ability to handle this volume increase.

Moreover, the use of AI in scientific production requires reflection on standards and quality criteria to ensure that technological advancements serve to enrich science rather than compromise it. This involves balancing innovation with maintaining high scientific standards.