Do Neural Networks Preserve Case Structure? Case-Based Decomposition, Interpretation, and Decision Consistency
Neural networks increasingly inform consequential decisions, making their reliability increasingly important. Yet their internal mechanisms provide little evidence of whether decisions remain grounded in the training cases and which cases ultimately support or oppose their outcomes. Without this connection between decisions and training cases, users cannot determine whether a model has learned reliable decision patterns from data. This motivates a fundamental question: do neural networks preserve case structure? We establish a connection between neural networks and Case-Based Decision Theory (CBDT), showing that trained neural networks can preserve a recoverable case structure through the...
| Authors | Manli Yan, Yaowen Yu, Yong Zhao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 July 2026 |
| Last updated at source | 25 September 2026 |
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