https://github.com/feedzai/xai_finance_tutorial
https://github.com/feedzai/xai_finance_tutorial
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/feedzai/xai_finance_tutorial
- Owner: feedzai
- Created: 2021-06-29T08:53:43.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-10-05T22:24:06.000Z (over 4 years ago)
- Last Synced: 2025-06-18T18:04:58.619Z (about 1 year ago)
- Size: 12.7 KB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# [Explainable AI in Financial Services](https://feedzai.github.io/xai_finance_tutorial/)
## Slides
Slides are available [here](https://docs.google.com/presentation/d/1B1_mDxHP5btkOB-tnk0Cp5HNVjMeCv1IOmeXH9RXTy8/edit?usp=sharing).
## Contributors
* Sergio Jesus - Feedzai, DCC-FCUP Universidade do Porto
* João Bento - Feedzai
* Vladimir Balayan - Feedzai
* Catarina Belém - Feedzai
* André Cruz - Feedzai
* Pedro Saleiro - Feedzai
* Pedro Bizarro - Feedzai
## References
[1] V. Balayan, P. Saleiro, C. Belém, L. Krippahl, and P. Bizarro, “Teaching the machine to explain itself using domain knowledge,” in NeurIPS 2020 Workshop on Human And Model in the Loop Evaluation and Training Strategies, 2020. Available: https://hamlets-workshop.github.io/schedule/
[2] S. Jesus, C. Belém, V. Balayan, J. Bento, P. Saleiro, P. Bizarro, and J.Gama, “How can i choose an explainer? an application-grounded evaluation of post-hoc explanations,” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, Association for Computing Machinery, 2021, pp. 805–815. Available:https://doi.org/10.1145/3442188.3445941.
[3] C. Belém, V. Balayan, P. Saleiro, and P. Bizarro, “Weakly supervised multi-task learning for concept-based explainability,” in International Conference on Learning Representations 2021 Workshop on Weakly Supervised Learning, 2021. Available:https://weasul.github.io/accpapers/.
[4] J. Bento, P. Saleiro, A. F. Cruz, M. A. T. Figueiredo, and P. Bizarro, "TimeSHAP: Explaining Recurrent Models through Sequence Perturbations", in Proc. of the 27th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, (KDD), 2021.