The increasing integration of AI into scientific research signifies an important epistemological shift, challenging long-standing paradigms in how knowledge is created, validated, and understood. Traditionally, the scientific process has been rooted in human observation, hypothesis formation, experimentation, and theoretical analysis. With AI systems like Google DeepMind’s research assistant and BioNTech’s Laila now taking on key roles in these processes, the nature of scientific inquiry is being transformed.

Historically, science has relied on human-driven methodologies. The scientific method — comprising hypothesis formulation, empirical testing, and theoretical explanation — has centered around human cognition.

AI, however, is reshaping this ecosystem by automating parts of the scientific method, particularly in areas such as hypothesis generation, predictive modelling, and data analysis. This raises an essential epistemological concern: Can knowledge generated by AI be regarded as genuine scientific understanding, or is it merely statistical inference?

Paul Humphreys has argued that while AI systems can identify patterns at a scale and complexity far beyond human capabilities, they lack the conceptual understanding that human scientists bring to bear. AI operates through computational methods such as inductive reasoning and deep learning algorithms, but it does not possess what philosophers call “intentionality” — the ability to grasp meaning and context.

This absence of understanding brings out a critical difference between human-generated and AI-generated knowledge, leading us to question whether AI’s contributions can be equated with genuine epistemic insight.

AI systems such as DeepMind’s research assistant may suggest hypotheses or predict outcomes based on vast datasets, but they do so without any deeper conceptual comprehension. While this pattern-based approach can lead to useful predictions, the lack of understanding presents a significant epistemological problem. Scientific knowledge is not just about finding correlations — it is about grasping the underlying principles and mechanisms. AI, however, lacks the capability to generate theoretical explanations, which casts doubt on the authenticity of its contributions to scientific inquiry.

Reliability of output

One of the most significant epistemological challenges posed by AI in science is the reliability of its outputs. AI models, particularly those based on neural networks and deep learning, often function as “black boxes.” This means that while they can produce highly accurate predictions, their internal decision-making processes are opaque, even to their creators.

In traditional scientific inquiry, transparency, reproducibility, and falsifiability are cornerstones of reliable knowledge. However, with AI, these principles are harder to maintain.

As Tim Miller and Margaret Mitchell argue, the lack of explainability in AI systems is a major hurdle for their reliable application in scientific contexts. Without transparency, it is difficult to evaluate whether the AI’s conclusions are based on sound reasoning or on spurious correlations.

Moreover, AI systems are only as reliable as the data they are trained on. If the training data contains biases or errors, those flaws can propagate through the AI model, leading to flawed conclusions. This creates an epistemic risk: scientists may place undue trust in AI-generated insights without fully understanding their limitations.

Additionally, the issue of reproducibility — a core element of scientific rigour — becomes more complex with AI. If the processes that lead to a particular conclusion cannot be fully understood or replicated, the reliability of that knowledge is compromised. This challenge is particularly acute in fields such as biology and medicine, where AI-generated insights directly influence decisions about public health or treatment strategies.

As AI systems take on more active roles in scientific research, the role of human scientists is changing. Rather than being the primary drivers of discovery, scientists may become interpreters or curators of AI-generated hypotheses and results. This shift has profound implications for human agency in science.

Evan Selinger’s work on automation highlights the risk of deskilling in scientific fields, where human expertise may erode as tasks are increasingly delegated to machines. In this scenario, scientists might rely on AI-generated insights without critically engaging with the underlying data or algorithms. Over time, this could lead to a reduction in the creative and interpretative aspects of science, which are essential for breakthrough discoveries.

Furthermore, the psychological phenomenon of automation bias — the tendency to favour automated decisions over human judgment — could exacerbate this problem. If scientists defer to AI systems too readily, without critically scrutinising the AI’s reasoning or outputs, there is a risk that science could become less reflective and more mechanised. This diminishes the active, interpretive role that scientists have historically played in the pursuit of knowledge.

The danger is that AI could lead to a form of scientific pragmatism where predictive accuracy is prioritised over explanatory depth. While this may be useful for solving practical problems, it could also signal a departure from the traditional goals of science, which have always been centred on the pursuit of truth.

Misleading perception

AI systems are often perceived as objective and neutral, but this perception is misleading. AI is inherently shaped by the biases present in its training data and the algorithms designed by humans. As Kate Crawford and Ryan Calo have noted, the apparent objectivity of AI can mask these underlying biases, leading to flawed outcomes.

In scientific research, biased AI models could skew findings and introduce systematic errors. This is particularly concerning in fields like medicine, where biased AI could lead to incorrect diagnoses or ineffective treatments. The illusion of objectivity created by AI’s data-driven nature can lull scientists into overlooking these biases, further compounding the problem. Ultimately, AI represents a new paradigm in science — one that shifts the balance between human-driven discovery and machine-assisted prediction. As we move forward, it will be essential to ensure that AI’s predictive power is balanced with a continued commitment to causal understanding and theoretical depth. Only then can AI truly enhance, rather than undermine, the scientific endeavour.

The writer is Officer on Special Duty, Research, Economic Advisory Council to the Prime Minister. Views are personal