Re-engineering the Concept of Scientific Discovery in the Age of AI

Artificial intelligence (AI) is transforming the landscape of scientific inquiry challenging traditional paradigms and raising questions about the nature of scientific discovery. As AI systems evolve, their integration into scientific methods marks a revolutionary shift, providing unprecedented opportunities for scientific progress across various disciplines. This project aims to explore these changes, addressing the role of AI as an agent of discovery and its implications for scientific practice and philosophical views on scientific discovery. In particular, this project addresses key epistemic questions: What epistemic virtues do AI systems exhibit that enable them to act as agents of discovery? How do these virtues influence the nature of knowledge generated by AI-driven discoveries? How should we conceptualize scientific discovery, in light of the pivotal role of AI systems as agents of scientific discoveries? A better understanding of the concept of scientific discovery in the age of AI-driven science is beneficial to ensuring that AI-based scientific discovery are robust, objective, and that they truly generate novel and genuine scientific knowledge.

AI, scientific objectivity, and the well-being of science

The integration of artificial intelligence (AI) into the contemporary scientific method constitutes a revolutionary change in the progress of science. Its pervasive role in scientific research includes several crucial tasks, including the collection and creation of data, their refinement and extrapolation of meaningful representations from them, the generation of new hypotheses, and experimental design and optimization. AI-based tools are also crucial for the development of automated experimentation platforms and the creation of self-driving laboratories.  

In the last two years, AI has become fundamental for revolutionary scientific discoveries in various fields including physics. A prominent example is the success of NASA's Kepler space telescope, which discovered the existence of 300 exoplanets, previously completely unknown, using the deep learning classifier Exo-Miner (Valizadegan et al. 2022). The development of deep learning is also proceeding at an incredible pace with regard to its application to the detection of gravitational waves (Huerta et al. 2021). In the field of materials physics, scientists at Brookhaven National Laboratory have successfully used AI-based autonomous methods to support a self-assembly technique to discover, without any human intervention, three new nanostructures, including a first-of-its-kind structure (Doerk et al. 2023).  

What does the future of science look like? What will be the impact of the increasing integration of AI methods on scientific knowledge? And can AI really achieve epistemic successes such as scientific understanding or a scientific objectivity? As Messeri and Crockett (2023) have pointed out, the risk is that AI not only does not provide greater scientific understanding, but that it does not provide scientific objectivity at all. In fact, the fear is that the type of objectivity that characterizes AI is fake because it is partial. The AI systems that are often integrated into scientific research are typically designed on the basis of an optimization process, and therefore are aimed at solving problems in the most efficient way, identifying the most likely solution, i.e. the one that provides the most accurate predictions. This factor and the ongoing process of replacing human capital with AI risk leaving science in a state of "monoculture", which is partial and even dangerous because it leads to the depletion of the fertile and diverse intellectual ground on which science has historically thrived, fueled by a variety of scientific methods and cognitive resources.

However, this risk can be mitigated and controlled. My proposal is that the Achilles heel of AI, which is its relying, typically, on an optimization process, can become an asset: the optimization process, in fact, can be designed, with due caution, in such a way that it takes into account or even manages to implement the well-being of science.

Understanding with Machine Learning

Whether machine learning (ML) algorithms can provide scientific understanding is a pressing question given their increasingly central role in the scientific method. In this project, I explore this issue through an investigation of ML’s application in accelerator research, with a particular focus on how it is implemented to the electron beam ion source (EBIS) at Brookhaven National Laboratory (BNL). More specifically, I examine how BNL physicists are employing two ML packages, on the one hand the GPTune to optimize EBIS beam intensity in real time, and on the other hand the XGBoost to analyze and interpret EBIS experimental data. This is a philosophically interesting case, as BNL scientists argue that ML algorithms not only enhance experimental optimization but also provide genuine understanding of the underlying physical system (Gu et al. 2024), a claim that may appear suspicious to philosophers. I will argue that while ML algorithms may not offer a kind of understanding that appeal to law-like explanations, they do furnish a form of understanding, understood as an intellectual virtue, which I term ‘attributive’.