Hot Topics in Explainable Machine Learning and Artificial Intelligence (XAI)

Overview

SemesterSummer 2024
Course typeBlock Seminar
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits4 ECTS
Room148, Building 50.34
LanguageEnglish
Linkhttps://ilias.studium.kit.edu/goto.php?target=crs%5F2359844&client_id=produktiv
RegistrationTBA

Description

This seminar is concerned with explainable machine learning in computer security. Learning-based systems often are difficult to interpret, and their decisions are opaque to practitioners. This lack of transparency is a considerable problem in computer security, as black-box learning systems are hard to audit and protect from attacks.

The module introduces students to the emerging field of explainable machine learning and teaches them to work up results from recent research. To this end, the students will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.

Topics cover different aspects of the explainability of machine learning methods for the application in computer security in particular.

Schedule

DateStep
Tue, 16. April, 9:45–11:15Kick-off \& Topic presentation
Thu, 18. April, 11:59 (noon)Send topic selection
(assignment happens till 15:00)
Fri, 19. April, 11:59 (noon)Officially register for assigned topic
(missed opportunities will be reassigned to waiting list till 15:00)
Tue, 23. April, 9:45–11:15Optional unit on "How to Ace the Seminar"
Thu, 25. AprilArrange appointments with assistant
Mon, 29. April - Fri, 03. May1st individual meeting (Provide first overview and ToC)
Mon, 10. June - Fri, 14. June2nd individual meeting (Feedback on draft report)
Wed, 26. JuneSubmit final paper
Mon, 08. JulySubmit review for fellow students
Fri, 12. JulyEnd of discussion phase
Fri, 19. JulySubmit camera-ready version of your paper
Fri, 26. JulyPresentation at final colloquium

Matrix Chat

News about the seminar, potential updates to the schedule, and additional material are distributed using the course's matrix room. Moreover, matrix enables students to discuss topics and solution approaches.

You find the link to the matrix room on ILIAS.

Topics

Every student may choose one of the following topics. For each of these, we additionally provide recent top-tier publications that serve as the basis for the seminar report. For the seminar and your final report, you should not merely summarize these papers, but try to go beyond and arrive at your own conclusions.

  • CAM-based Explanations

  • Visual Counterfactual Explanations

  • Perturbation-based Explanations

  • Measuring the Quality of Explanations

  • The Theory of Explanations

  • Explainable AI in Adversarial Environments

  • Concept-Based Explanations

  • Utility of Explainability in LLMs