Abstract
In Assicurazioni Generali, an automatic decision-making model is used to check real-time multivariate time series and alert if a car crash happened. In such a way, a Generali operator can call the customer to provide first assistance. The high sensitivity of the model used, combined with the fact that the model is not interpretable, might cause the operator to call customers even though a car crash did not happen but only due to a harsh deviation or the fact that the road is bumpy. Our goal is to tackle the problem of interpretability for car crash prediction and propose an eXplainable Artificial Intelligence (XAI) workflow that allows gaining insights regarding the logic behind the deep learning predictive model adopted by Generali. We reach our goal by building an interpretable alternative to the current obscure model that also reduces the training data usage and the prediction time.
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Notes
- 1.
Details can not be disclosed due to company policies.
- 2.
The subsequence extraction is performed using the default implementation parameters for MR-SEQL and the heuristic proposed in [2] for LTS.
- 3.
All the models are trained using the default library implementation parameters: Scikit-learn for dt, rf, XGBoost for xgb, LightGBM for lgb, CatBoost for cat.
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Acknowledgment
This work has been partially supported by the European Community Horizon 2020 programme under the funding schemes: G.A. 871042 SoBigData++, G.A. 952026 HumanE AI Net, and G.A. 834756 XAI.
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Spinnato, F., Guidotti, R., Nanni, M., Maccagnola, D., Paciello, G., Farina, A.B. (2022). Explaining Crash Predictions on Multivariate Time Series Data. In: Pascal, P., Ienco, D. (eds) Discovery Science. DS 2022. Lecture Notes in Computer Science(), vol 13601. Springer, Cham. https://doi.org/10.1007/978-3-031-18840-4_39
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