ServiceNow AI Research

Interpretability

Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale …
Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. …
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

Recent metabolomics measurement devices, such as mass spectrometers, produce extremely high-dimensional data. Together with small …

Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around …
Interpretable genotype-to-phenotype classifiers with performance guarantees
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, …