The Explainability-Scalability Trade-off: Bridging the Gap Between Opaque Deep Learning and Interpretable Systems

The widespread integration of artificial intelligence into critical sectors has created a fundamental conflict between the superior predictive power of scalable but opaque "black-box" models and the ethical and regulatory demand for transparent decision-making. This analysis investigates the explainability-scalability trade-off by examining its underlying mathematical, architectural, and theoretical foundations. Inherently interpretable models, such as decision trees, often fail to scale effectively due to structural constraints, such as the curse of dimensionality, which causes data fragmentation in high-dimensional spaces. In contrast, deep learning architectures achieve unparalleled performance by using distributed representations to disentangle complex data manifolds, a success governed by predictable empirical scaling laws and enabled by synergies with modern parallel hardware. Theoretical frameworks, including the Information Bottleneck theory, posit that this opacity is a prerequisite for optimal generalisation, as models achieve accuracy by compressing data into abstract, non-human-readable forms. The study critically evaluates the common practice of using post-hoc explanation methods, revealing their lack of faithfulness to a model's true logic and their susceptibility to adversarial attacks designed to hide underlying biases. Arguing that the trade-off is not a universal law, particularly for structured data in high-stakes domains, the text advocates for a paradigm shift. Instead of retrofitting explanations onto opaque systems, the future lies in developing inherently interpretable architectures, such as Neural Additive and Concept Bottleneck Models. These models embed transparency into their design, aiming to deliver high predictive accuracy while satisfying the growing legal mandates for a "right to explanation," thereby resolving the tension between computational power and human comprehension.