Question

Passage A
Recent breakthroughs in deep neural networks have ignited a foundational debate regarding what constitutes legitimate scientific progress. In fields ranging from structural biology to climatology, complex algorithmic models now predict natural phenomena—such as the three-dimensional folding of proteins from linear amino acid sequences—with an accuracy that vastly exceeds the capabilities of traditional theoretical frameworks. Despite this success, skeptics argue that because these models operate as computational "black boxes," generating predictions without revealing the intermediate physical steps, they provide practical utility without generating true scientific understanding.

This critique, however, rests on an obsolete, anthropocentric philosophy of science. The historical insistence that valid scientific theories must consist of human-interpretable mechanistic principles was an epistemological compromise necessitated by human cognitive limits, not an intrinsic feature of the universe. Complex physical and biological systems often involve millions of interacting variables whose nonlinear dynamics fundamentally resist reduction to neat, intuitive laws. When an algorithmic model demonstrates reliable predictive power across novel, out-of-sample data, it has fulfilled the essential objective of empirical science: the accurate forecasting of natural behavior. To insist that a model is scientifically deficient merely because human minds cannot trace its internal computational pathways is to confuse human cognitive convenience with objective validity.

Passage B
The increasing adoption of complex predictive algorithms in the natural sciences threatens to erode the crucial distinction between empirical forecasting and genuine scientific explanation. While deep neural networks have achieved remarkable success in predicting complex outcomes such as molecular configurations, predictive accuracy alone should not be conflated with scientific comprehension. Science has never been solely an exercise in output generation; its core mission is explanatory—identifying the underlying causal mechanisms that reveal why a phenomenon occurs and how it relates to broader natural principles.

Without causal interpretability, a predictive model remains scientifically fragile. Because "black box" architectures identify statistical correlations rather than causal structures, researchers cannot determine why the model succeeds, identify its structural blind spots, or assess how the system would behave under counterfactual interventions. When such models are applied outside their training parameters, their predictive efficacy often collapses without warning, precisely because they lack an understanding of governing physical laws. Moreover, historical breakthroughs in science have consistently occurred when researchers abstracted generalizable principles from specific mechanisms, thereby uniting seemingly disconnected phenomena. Abandoning the pursuit of mechanistic explanation in favor of computational prediction risks reducing natural science to a sophisticated form of atheoretical engineering—producing tools that work reliably in practice while leaving scientists in the dark about the world they seek to understand.

Based on the passages, the author of Passage A and the author of Passage B would be most likely to disagree about which one of the following assertions?