The Accuracy-Interpretability Dilemma: A Strategic Framework for Navigating the Trade-off in Modern Machine Learning

This paper explores the enduring accuracy-interpretability trade-off in machine learning, highlighting its profound implications for model selection, regulatory compliance, and practical deployment across diverse industries. It begins by defining accuracy as a model’s ability to generalise effectively on unseen data, measured through context-specific metrics. It contrasts it with interpretability, which ensures that model predictions are understandable and justifiable to human stakeholders. The paper maps models across the white-box to black-box spectrum, from inherently transparent techniques such as linear regression and decision trees to opaque but highly accurate methods like ensemble models and deep neural networks. It critiques the conventional view that increasing accuracy necessarily diminishes interpretability, presenting alternative perspectives such as the Rashomon effect, which suggests that equally accurate yet interpretable models often exist within the solution space. The paper emphasises two pathways: interpretability-by-design approaches, such as Generalised Additive Models and sparse decision trees, and post-hoc explainability tools like LIME and SHAP that enhance transparency in black-box models. Industry case studies in finance, healthcare, algorithmic trading, and business strategy illustrate the context-dependent balance between performance and explainability, shaped by legal mandates, trust requirements, and operational priorities. The framework proposed equips practitioners with strategic questions to guide model selection, incorporating considerations of compliance, end-user needs, and the relative costs of errors versus missed insights. The paper also anticipates future advancements in Explainable AI, inherently interpretable architectures, and causal machine learning that could dissolve the trade-off altogether by achieving high accuracy without sacrificing transparency. By reframing the dilemma as a strategic decision rather than a rigid constraint, it provides a structured roadmap for aligning model development with business objectives, ethical imperatives, and stakeholder trust, advocating a shift towards accuracy and interpretability as complementary rather than competing goals.