Position: Explain to Question not to Justify

Przemyslaw Biecek, Wojciech Samek
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:3996-4006, 2024.

Abstract

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-biecek24a, title = {Position: Explain to Question not to Justify}, author = {Biecek, Przemyslaw and Samek, Wojciech}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {3996--4006}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/biecek24a/biecek24a.pdf}, url = {https://proceedings.mlr.press/v235/biecek24a.html}, abstract = {Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.} }
Endnote
%0 Conference Paper %T Position: Explain to Question not to Justify %A Przemyslaw Biecek %A Wojciech Samek %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-biecek24a %I PMLR %P 3996--4006 %U https://proceedings.mlr.press/v235/biecek24a.html %V 235 %X Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.
APA
Biecek, P. & Samek, W.. (2024). Position: Explain to Question not to Justify. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:3996-4006 Available from https://proceedings.mlr.press/v235/biecek24a.html.

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