Seminar explainable AI - Human-computer interaction meets Artificial Intelligence

  • Teaching


    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code UE-SIN.08813
    Languages English
    Type of lesson Seminar
    Level Master
    Semester SP-2023

    Schedules and rooms

    Summary schedule Friday 13:15 - 15:00, Cours bloc, PER 21, Room G314 (Spring semester)
    Struct. of the schedule 2h par semaine durant 14 semaines
    Contact's hours 28



    With the increasing use of automation, users tend to delegate more and more tasks to the machines. Complex systems are usually developed with an Artificial Intelligence (AI) and can embed different kinds of models and algorithms including Machine Learning and Deep Learning, which make these systems difficult to understand for the user. This assumption is for instance particularly true in the field of automated driving since the level of automation is increasing or in the health domain where more and more sophisticated AI powered diagnostic tools are used every day. In order to better understand how AI works and build trust in the decisions made by AIs, new techniques in the field are emerging that are referred to as Explainable AI (XAI) [1].
    These techniques are intended to make AI transparent and the contents of the "black boxes" accessible. The main purposes of this transparency are to:
    - understand the functioning of algorithms and AIs in order to optimize their design and architecture, their features but also to understand and interpret the results
    - increase human confidence in systems
    - increase and improve cooperation between agents
    As shown by [2], providing appropriate explanations to the user increases the user’s confidence in the system and thus allows for better human-IA collaboration.

    The goal of this seminar is to investigate the field of Explainable AI (XAI) with a particular focus on the perspective of human interaction since it has not been sufficiently studied in existing explainable approaches [1] [3]. The seminar will address the topics related to the design of human-computer interfaces for XAI. Effective knowledge transfer through an explanation depends on a combination of AI algorithms used, explanation dialogues, and interfaces that can accommodate explanations.
    Questions like “what kind of explanation do we need”, “what an explanation should look like? ”, “which is the best trade-off between performance and explainability we want to achieve”, “how granular should the explanations be” and “how to evaluate explanations” will be investigated in this seminar.

    This seminar will help the students to improve their research and practical abilities. It will have a strong practical component as students will investigate existing applications as well as develop new concepts in the aforementioned domains.

    Training objectives

    - Identify and illustrate existing approaches in Explainable AI.
    - Discuss and compare different methods for increasing system interpretability and transparency.
    - Evaluate and select the best existing interactions and interfaces for intelligibility
    - Identify and describe different ways of evaluating system explanability, accountability and intelligibility
    - Identify and describe how to design interfaces to increase AI system predictability


    MSc-CS BENEFRI - (Code Ue: 33526 / Tracks: T3, Code Ue: 63526 / T6) The exact date and time of this course as well as the complete course list can be found at

    Course and exam registration on ACADEMIA (not Please follow the instructions on

    Off field
  • Dates and rooms
    Date Hour Type of lesson Place
    10.03.2023 13:15 - 15:00 Cours PER 21, Room G314
    21.04.2023 13:15 - 15:00 Cours PER 21, Room G314
    28.04.2023 13:15 - 15:00 Cours PER 21, Room G314
    26.05.2023 13:15 - 16:00 Cours PER 21, Room G314
  • Assessments methods

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    Assessments methods By rating
  • Assignment
    Valid for the following curricula:
    Additional Courses in Sciences
    Version: ens_compl_sciences
    Paquet indépendant des branches > Specialized courses in Computer Science (Master level)

    Additional programme requirements for PhD studies [PRE-DOC]
    Version: 2020_1/v_01
    Additional programme requirements for PhD studies (Faculty of Science and Medicine) > Specialized courses in Computer Science (Master level)

    Computer Science [3e cycle]
    Version: 2015_1/V_01
    Continuing education > Specialized courses in Computer Science (Master level)

    Computer Science [POST-DOC]
    Version: 2015_1/V_01
    Continuing education > Specialized courses in Computer Science (Master level)

    MSc in Computer science (BeNeFri)
    Version: 2010_2/V_02
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > Specialized courses in Computer Science (Master level)