Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models
<|reference_start|>Model Interpretation and Explainability: Towards Creating Transparency in Prediction Models: Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction models. We analyze a dataset of loans from a credit card company and apply three stages: execute and compare four different prediction methods, apply the best known explainability techniques in the current literature to the model training sets to identify feature importance (FI) (static case), and finally to cross-check whether the FI set holds up under what if prediction scenarios for continuous and categorical variables (dynamic case). We found inconsistency in FI identification between the static and dynamic cases. We summarize the state of the art in model explainability and suggest further research to advance the field.<|reference_end|>
@article{kridel2024model,
    title={Model Interpretation and Explainability: Towards Creating Transparency
  in Prediction Models},
    author={Donald Kridel, Jacob Dineen, Daniel Dolk, David Castillo},
    journal={arXiv preprint arXiv:2405.20794},
    year={2024},
    doi={10.24251/HICSS.2020.120},
    archivePrefix={arXiv},
    eprint={2405.20794},
    primaryClass={cs.LG}
}