MASH-Bench: Diagnosing Cross-Source Failure in Mass-Shooting Risk Classification
Public mass-shooting databases differ substantially in coverage, feature availability, and reporting practices, creating challenges for machine-learning models that must generalize across data sources. We introduce MASH-Bench, a harmonized benchmark of 6,968 incidents from four U.S. databases: Kaggle, Mother Jones, Stanford MSA, and the Gun Violence Archive (GVA). We evaluate cross-source risk classification using leave-one-dataset-out (LODO) evaluation. Random Forest, XGBoost, and LightGBM achieve VeryHigh-risk recall of 0.68-0.89 on the curated sources but generalize poorly to GVA, where mean recall drops to 0.20 and precision to 0.0004. To investigate the source of this degradation, we...
| Authors | Neha Sharma, Ritesh Sharma |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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