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See detailOn the Suitability of SHAP Explanations for Refining Classifications
Arslan, Yusuf UL; Lebichot, Bertrand UL; Allix, Kevin UL et al

in In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) (2022, February)

In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in ... [more ▼]

In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in an ML pipeline is paramount to reduce the workload of experts while increasing customers’ trust. Increasingly, SHAP Explanations are leveraged to facilitate manual analysis. Because they have been shown to be useful to human analysts in the detection of false positives, we postulate that SHAP Explanations may provide a means to automate false-positive reduction. To confirm our intuition, we evaluate clustering and rules detection metrics with ground truth labels to understand the utility of SHAP Explanations to discriminate false positives from true positives. We show that SHAP Explanations are indeed relevant in discriminating samples and are a relevant candidate to automate ML tasks and help to detect and reduce false-positive results. [less ▲]

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See detailA Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain
Arslan, Yusuf UL; Allix, Kevin UL; Veiber, Lisa UL et al

in Companion Proceedings of the Web Conference 2021 (WWW '21 Companion), April 19--23, 2021, Ljubljana, Slovenia (2021, April 19)

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See detailChallenges Towards Production-Ready Explainable Machine Learning
Veiber, Lisa UL; Allix, Kevin UL; Arslan, Yusuf UL et al

in Veiber, Lisa; Allix, Kevin; Arslan, Yusuf (Eds.) et al Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20) (2020, July)

Machine Learning (ML) is increasingly prominent in or- ganizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating ... [more ▼]

Machine Learning (ML) is increasingly prominent in or- ganizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating regulation in Artificial Intelligence (AI) and deepening user awareness, explainability has become a priority notably in critical healthcare and financial environ- ments. The various frameworks developed often overlook their integration into operational applications as discovered with our industrial partner. In this paper, explainability in ML and its relevance to our industrial partner is presented. We then dis- cuss the main challenges to the integration of ex- plainability frameworks in production we have faced. Finally, we provide recommendations given those challenges. [less ▲]

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