Integrating explanation and prediction in computational social science

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A Deep Gravity model for mobility flows generation (2020, background): “Understanding why a model makes a certain prediction is crucial to interpret results, explain differences between models, and assess to what extent we understand the phenomenon under analysis 67 – 69 .”
Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “Epistemic risks arise here from failing to appreciate the largely invisible “researcher degrees of freedom” that are embedded in the process of approximation 113 .”
Leakage and the reproducibility crisis in machine-learning-based science (2023, methodology): “This is despite the fact that the LR models were not trained to optimize predictive accuracy: they were conceived as explanatory models to understand past conflicts instead of predicting future ones.(47,81,82) We test our model info sheets on the four civil war prediction papers with errors and find”