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The Rash\=omon effect poses challenges for deriving reliable knowledge frommachine learning models. This study examined the influence of sample size onexplanations from models in a Rash\=omon set using SHAP. Experiments on 5public datasets showed that explanations gradually converged as the sample sizeincreased. Explanations from <128 samples exhibited high variability, limitingreliable knowledge extraction. However, agreement between models improved withmore data, allowing for consensus. Bagging ensembles often had higheragreement. The results provide guidance on sufficient data to trustexplanations. Variability at low samples suggests that conclusions may beunreliable without validation. Further work is needed with more model types,data domains, and explanation methods. Testing convergence in neural networksand with model-specific explanation methods would be impactful. The approachesexplored here point towards principled techniques for eliciting knowledge fromambiguous models.

Can we Agree? On the Rash\=omon Effect and the Reliability of Post-Hoc Explainable AI