arXiv — cs.AI preprintsInternational9 October 2026
Explanation Multiplicity in SHAP: Characterization and Assessment
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2601.12654v3 Announce Type: replace-cross Abstract: SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even when the model, the input instance, and the prediction are held fixed. Prior work has documented disagreement between explanation methods; we show that substantial disagreement arises even within SHAP across reruns of the same estimator on the same trained model and instance. We call this phenomenon explanation multiplicity and develop an evaluation methodology for characterizing it under deploymen
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment9 October 2026
- Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction9 October 2026
- Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice9 October 2026
- Verification and Self-Improvement in Agentic AI: Foundations and Limits9 October 2026
- The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate9 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.