Researchers developed a sparse recovery method to attribute model predictions back to specific training examples, addressing a core interpretability gap in deep learning systems. The approach enables practitioners to identify which training data points most influenced individual model outputs. For compliance and safety teams, this capability directly supports audit trails and model documentation requirements—particularly relevant under emerging regulations requiring transparency on training composition. For debugging, teams can now isolate whether model failures stem from poisoned data, distribution shifts, or specific problematic examples rather than conducting blind retraining cycles. Operationally, this shifts data curation from reactive (retraining on suspicion) to targeted (removing or correcting identified problematic examples). Teams working on high-stakes deployments can reduce validation costs by focusing human review on training examples that actually drive consequential predictions, rather than auditing entire datasets. This also enables faster root-cause analysis when models exhibit unexpected behaviors in production.