While these goals may seem aligned, tension arises in practice. Models optimized for prediction often employ complex, high-dimensional algorithms that lack interpretability, limiting their ability to offer explanations. On the other hand, explanatory models are typically simpler and more interpretable but may sacrifice accuracy in predicting future events.
The perceived dichotomy between prediction and explanation is exemplified in machine learning and traditional scientific inquiry. Machine learning, with its focus on optimizing predictive accuracy, often employs "black-box" models like neural networks, which excel at prediction but are criticized for their opacity (Rudin, 2019). In contrast, traditional science has valued parsimonious models that provide clear, interpretable explanations but may struggle with generalizability and predictive performance.
However, separating these two goals can create blind spots. For example, a model that predicts well without understanding underlying mechanisms may inadvertently reinforce biases, as seen in some AI systems (Obermeyer et al., 2019). Conversely, purely explanatory models risk being dismissed if they fail to provide accurate forecasts, reducing their practical applicability.
Bridging the Gap: Toward Integrated Frameworks
Recent research suggests that predictive and explanatory power should not be viewed as competing goals but as complementary facets of robust scientific modeling. Some propose the concept of "interpretability in machine learning" as a bridge, which aims to design predictive models that are also interpretable and explanatory (Doshi-Velez & Kim, 2017). These models balance complexity with clarity, enabling better decision-making in fields like healthcare and environmental science.
For instance, interpretable AI models have been developed to predict patient outcomes while explaining the factors influencing those predictions, improving both accuracy and trustworthiness (Caruana et al., 2015). This integration not only enhances the practical utility of models but also fosters ethical and transparent applications.
For instance, interpretable AI models have been developed to predict patient outcomes while explaining the factors influencing those predictions, improving both accuracy and trustworthiness (Caruana et al., 2015). This integration not only enhances the practical utility of models but also fosters ethical and transparent applications.
Beyond the Dichotomy: A Unified Perspective
A unified perspective on predictive and explanatory power recognizes that both serve vital roles in advancing knowledge and solving real-world problems. Prediction provides immediate utility by enabling actionable insights, while explanation ensures that models remain grounded in reality and can adapt to new contexts.
Fields like causal inference have demonstrated that models can simultaneously achieve high predictive accuracy and robust explanatory power. For example, Judea Pearl’s framework for causal diagrams integrates data-driven predictions with causal explanations, bridging the gap between the two paradigms (Pearl, 2009). This approach underscores the importance of using prediction to validate explanations and vice versa.
Fields like causal inference have demonstrated that models can simultaneously achieve high predictive accuracy and robust explanatory power. For example, Judea Pearl’s framework for causal diagrams integrates data-driven predictions with causal explanations, bridging the gap between the two paradigms (Pearl, 2009). This approach underscores the importance of using prediction to validate explanations and vice versa.
Conclusion
The dichotomy between predictive and explanatory power is an oversimplification that can hinder progress in both science and technology. By embracing frameworks that integrate both predictive accuracy and explanatory clarity, researchers can develop models that are not only effective in forecasting but also provide valuable insights into underlying mechanisms. This holistic approach offers a path forward, ensuring that scientific inquiry and technological innovation remain both impactful and accountable.