Soru

Passage A
The integration of deep neural networks (DNNs) into high-stakes domains—such as medical diagnostics, autonomous navigation, and criminal justice—has precipitated a severe crisis of interpretability. Unlike traditional algorithmic models, such as decision trees or linear regressions, which operate via transparent, rule-based logic, DNNs function as highly parameterized "black boxes." They map inputs to outputs through millions of nonlinear transformations across convoluted hidden layers, rendering the intermediate representations largely inscrutable to human analysts. This opacity is not merely an engineering bug, but a foundational feature of modern connectionist architectures; the very same distributed representations that grant these models their extraordinary predictive accuracy simultaneously obscure their internal reasoning mechanisms. Consequently, when a diagnostic DNN recommends a drastic medical intervention, clinicians are unable to verify whether the model is relying on robust physiological markers or spurious statistical artifacts inadvertently captured during training. The epistemological burden this creates is profound. If human experts cannot audit the causative pathways leading to an algorithmic decision, then the deployment of such systems in life-critical scenarios relies on empirical faith rather than verifiable scientific reasoning, fundamentally undermining the rigid principles of accountability that traditionally govern professional domains. The result is a paradigm where predictive success outpaces our capacity to comprehend its mechanics, leaving practitioners vulnerable to catastrophic algorithmic failures that defy post-mortem analysis.

Passage B
In response to mounting regulatory and ethical pressure for algorithmic transparency, the machine learning community has increasingly turned to post-hoc explainability tools, most notably "saliency maps" and feature attribution methods. When a neural network classifies a medical image, a saliency map generates a colorful heatmap over the original input, ostensibly highlighting the specific pixels that most heavily influenced the network's final output. Proponents argue that these visual aids bridge the gap between complex connectionist logic and human understanding, providing clinicians with actionable insights into the model’s otherwise hidden diagnostic criteria. However, recent rigorous empirical evaluations suggest that this optimism is severely misplaced. Extensive studies have demonstrated that many popular saliency methods are alarmingly insensitive to the actual weights and structural parameters of the trained network; in some shocking cases, the generated heatmaps remain virtually unchanged even when the model's upper layers are completely randomized. Rather than revealing the true decision boundaries and computational pathways of the network, these tools often act as sophisticated, localized edge-detectors, simply highlighting visually salient features of the input image that look intuitively relevant to human observers. This phenomenon creates a dangerous illusion of interpretability. By offering superficial, post-hoc rationalizations that conveniently align with human cognitive biases, these tools can foster unwarranted trust in inherently opaque systems. Ultimately, these methods do not solve the foundational opacity of deep neural architectures; they merely paper over it, providing a deceptive sense of security that may actually exacerbate the systemic risks of deploying inscrutable models in sensitive environments.

Which one of the following most accurately describes the relationship between Passage A and Passage B?