Bachelor/Masterseminar (WS 2020/2021)

UnivIS.

Participants should sign up for the course in the virtual campus.

The course is usually offered in the winter term.

Course language is Geman by default.

Topic: 

The growing interest in machine learning in many application domains makes it necessary that machine learned models do not only have high predictive accuracy but also are transparent and comprehensible. Currently, we are on the way into the "third wave of AI" from "describe" over "learn" to "explain". There is a strong interdisciplinary interest in explanation generation for blackbox (mostly deep learning) models as well as a renaissance of interpretable machine learning approaches such as decision rules and inductive logic programming. In the seminar, we will discuss current research papers in the field.

Recommended Reading / Links / Topics

• Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.
• Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences (2018 arxiv). Artificial Intelligence. Volume 267, 1-38.
• Goodman, Bryce, and Seth Flaxman. "European Union regulations on algorithmic decision-making and a" right to explanation"." arXiv preprint arXiv:1606.08813 (2016).
• Molnar, C. (2018). Interpretable Machine Learning.
• Anna Monreale, Riccardo Guidotti, Pasquale Minervini, Salvo Rinzivillo: Tutorial on eXplainable Knowledge Discovery in Data Mining, ECML PKDD 2019, Joint International Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining. [download]
• Extracting tree-structured representations of trained networks. In Advances in neural information processing systems (pp. 24-30).
• Voskarides, N., Meij, E., Tsagkias, M., De Rijke, M., & Weerkamp, W. (2015, July). Learning to explain entity relationships in knowledge graphs. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 564-574).
• Kulesza, T., Burnett, M., Wong, W. K., & Stumpf, S. (2015, March). Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th international conference on intelligent user interfaces (pp. 126-137). ACM.
• Lipton, Z.C. (2016). The mythos of model interpretability. Int. Conf. Machine Learning: Workshop on Human Interpretability in Machine Learning. 2016.
• Muggleton, S. H., Schmid, U., Zeller, C., Tamaddoni-Nezhad, A., & Besold, T. (2018). Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Machine Learning, 107(7), 1119-1140.
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). 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 (pp. 1135-1144). ACM.
• Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.

Previous Seminars

Bachelor/Master-Seminar: Explainable Artificial Intelligence: [Bamberger Katalog (Universit?tsbibliothek)

FIS (Forschungsinformationssystem)

FlexNow2

Intranet

Office 365

Online-Dienste
(Studierendenkanzlei)

UnivIS 

Uni-Webmail:
https://mailex.uni-bamberg.de
https://o365.uni-bamberg.de

Virtueller Campus