Abstract
As humans, we automate more and more critical areas of our lives while using machine learning algorithms to make autonomous decisions. For example, these algorithms may approve or reject job applications/loans. To ensure the fairness and reliability of the decision-making process, a validation is required. The solution for explaining the decision process of ML models is Explainable Artificial Intelligence (XAI). In this paper, we evaluate four different XAI approaches - LIME, SHAP, CIU, and Integrated Gradients (IG) - based on the similarity of their explanations. We compare their feature importance values (FIV) and rank the approaches from the most trustworthy to the least trustworthy. This ranking can serve as a specific fidelity measure of the explanations provided by the XAI methods.
This work is supported with tax funds on the basis of the budget passed by the Saxon State Parliament.
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References
Amro, B., Najjar, A., Macido, M.: An intelligent decision support system for recruitment: resumes screening and applicants ranking (2022)
Anjomshoae, S., Kampik, T., Främling, K.: Py-CIU: a python library for explaining machine learning predictions using contextual importance and utility. In: IJCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence (XAI) (2020)
Anshari, M., Almunawar, M.N., Masri, M., Hrdy, M.: Financial technology with AI-enabled and ethical challenges. Society 58(3), 189–195 (2021). https://doi.org/10.1007/s12115-021-00592-w
Arya, V., et al.: One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques. CoRR abs/1909.03012 (2019). http://arxiv.org/abs/1909.03012
Bruckert, S., Finzel, B., Schmid, U.: The next generation of medical decision support: a roadmap toward transparent expert companions. Front. Artif. Intell. 3, 507973 (2020)
Cortinhas, S.: Credit card approvals (clean data) from kaggle (2022). https://www.kaggle.com/datasets/samuelcortinhas/credit-card-approval-clean-data. Accessed 16 Apr 2023
Das, A., Rad, P.: Opportunities and challenges in explainable artificial intelligence (XAI): a survey (2020). https://doi.org/10.48550/ARXIV.2006.11371. https://arxiv.org/abs/2006.11371
Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml. Accessed 16 Apr 2023
Främling, K.: Decision theory meets explainable AI. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) EXTRAAMAS 2020. LNCS (LNAI), vol. 12175, pp. 57–74. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51924-7_4
Främling, K.: Explainable AI without interpretable model. CoRR abs/2009.13996 (2020). https://arxiv.org/abs/2009.13996
Främling, K.: Contextual importance and utility: a theoretical foundation. In: Long, G., Yu, X., Wang, S. (eds.) AI 2022. LNCS (LNAI), vol. 13151, pp. 117–128. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-97546-3_10
Guo, H., Polak, P.: Artificial intelligence and financial technology FinTech: how AI is being used under the pandemic in 2020. In: Hamdan, A., Hassanien, A.E., Razzaque, A., Alareeni, B. (eds.) The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success. SCI, vol. 935, pp. 169–186. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-62796-6_9
Kaur, C., Garg, U.: Artificial intelligence techniques for cancer detection in medical image processing: a review. Mater. Today Proc. 81, 806–809 (2021)
Klaise, J., Looveren, A.V., Vacanti, G., Coca, A.: Alibi explain: algorithms for explaining machine learning models. J. Mach. Learn. Res. 22(181), 1–7 (2021). http://jmlr.org/papers/v22/21-0017.html
Liao, Q.V., Singh, M., Zhang, Y., Bellamy, R.: Introduction to explainable AI. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–3 (2021)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 4768–4777. Curran Associates Inc., Red Hook (2017)
Minh, D., Wang, H.X., Li, Y.F., Nguyen, T.N.: Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev. 55, 3503–3568 (2021)
Molnar, C.: Interpretable machine learning (2022). https://christophm.github.io/interpretable-ml-book/. Accessed 16 Apr 2023
Reddy, S., Allan, S., Coghlan, S., Cooper, P.: A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 27(3), 491–497 (2020)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1135–1144. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939778
Speith, T.: A review of taxonomies of explainable artificial intelligence (XAI) methods. In: 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 2239–2250 (2022)
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 3319–3328. JMLR.org (2017)
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Gawantka, F., Just, F., Ullrich, M., Savelyeva, M., Lässig, J. (2024). Evaluation of XAI Methods in a FinTech Context. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_13
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