Engineering decisions must remain understandable.
For this reason, explainability represents an important design objective for the Artificial Intelligence layer.
Recommendations should never appear as unexplained conclusions generated by an inaccessible computational process.
Instead, users should be able to understand which observations, environmental conditions and analytical models contributed to the recommendation.
Although highly sophisticated algorithms may occasionally reduce interpretability, Radixora deliberately favours engineering transparency whenever possible.
Reliable explanations are often more valuable than marginal improvements in predictive accuracy.