Artificial intelligence (AI) is establishing itself as an essential tool in the scientific field, revolutionizing research and discovery methods. The winners of the 2024 Nobel Prizes in Chemistry and Physics bear witness to this trend, having all integrated AI into their work.
AI promises to accelerate scientific discoveries, reduce costs and maximize research efficiency. However, this technology raises questions regarding understanding, public trust and scientific integrity. Experts warn against the illusions that the use of AI can create, such as the illusion of explanatory depth, width exploratory and objectivity.
One of the most striking examples of the use of AI in science is the development of a machine capable of producing scientific articles at a trivial cost. This approach, while attractive, risks overwhelming the scientific publishing system with low-quality work, thereby compromising the value and credibility of the research. Public trust in science is an essential element that should not be taken lightly. AI, by taking a prominent place in research, 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 therefore necessary to rethink the social contract of science. Scientists must engage in open discussions about the use of AI, taking into account its environmental impact, integrity and alignment with societal expectations. The objective is to ensure that science, enriched by AI, continues to serve the general interest and respond to current global issues.
AI thus represents an unprecedented opportunity for science, but its integration must be guided by in-depth reflection and close collaboration between scientists, decision-makers 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, while capable of accurately predicting certain phenomena, cannot explain the underlying mechanisms of those predictions. This can lead to erroneous conclusions about the nature of the phenomena studied, because predictive ability does not guarantee in-depth understanding.
This illusion is particularly problematic in fields like neuroscience, where AI models can predict outcomes based on data without necessarily reflecting actual biological processes. This highlights the importance of complementing AI predictions with human analysis and interpretation to avoid scientific misunderstandings.
Finally, the illusion of explanatory depth highlights the current limits of AI in scientific research, reminding us that the technology must be used as one tool among others, and not as a universal solution.
How does AI influence scientific production?
AI is transforming scientific production by enabling faster and less expensive 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 ability of the peer review system to handle this increase in volume.
Furthermore, the use of AI in scientific production requires consideration of quality standards and criteria, to ensure that technological advances serve to enrich science rather than undermine it. This involves a balance between innovation and maintaining high scientific standards.
