Teaching Explainable Artificial Intelligence to High School Students

被引：29
|
作者：
Alonso, Jose M. [1 ]
机构：
[1] Univ Santiago de Compostela Usc, Ctr Singular Invest Tecnoloxias Intelixentes CiTI, Santiago De Compostela, Spain
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2020年 / 13卷 / 01期
关键词：
Explainable artificial intelligence; Interpretable computational intelligence; Decision trees; l'uzzy rule-based classifiers; Educational AT resources; CLASSIFIERS;
D O I：
10.2991/ijcis.d.200715.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Artificial Intelligence (Al) is part of our everyday life and has become one of the most outstanding and strategic technologies. Explainable Al (XAI) is expected to endow intelligent systems with fairness, accountability, transparency and explanation ability when interacting with humans. This paper describes how to teach fundamentals of XAI to high school students who take part in interactive workshop activities at CiTIUS-USC. These workshop activities are carried out in the context of a strategic plan lot promoting careers on Science, Technology Engineering and Mathematics. Students learn (1) how to build datasets free of bias, (2) how to build interpretable classifiers and (3) how to build multi-modal explanations. (C) 2020 The ituthors. Published by Atlantis Press B.V.
引用
收藏
页码：974 / 987
页数：14
相关论文
共 50 条
  • [1] Teaching Explainable Artificial Intelligence to High School Students
    Jose M. Alonso
    [J]. International Journal of Computational Intelligence Systems, 2020, 13 : 974 - 987
  • [2] Design and Implementation of an Educational Game for Teaching Artificial Intelligence to High School Students
    Wang, Ning
    Montgomery, Ryan
    Greenwald, Eric
    Leitner, Maxyn
    [J]. 31ST INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2023, VOL I, 2023, : 579 - 588
  • [3] Lessons Learned From Teaching Artificial Intelligence to Middle School Students
    Touretzky, David
    Gardner-McCune, Christina
    Cox, Bryan
    Uchidiuno, Judith
    Kolodner, Janet
    Stapleton, Patriel
    [J]. PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 2, SIGCSE 2023, 2023, : 1371 - 1371
  • [4] On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout
    Melo, Elvis
    Silva, Ivanovitch
    Costa, Daniel G.
    Viegas, Carlos M. D.
    Barros, Thiago M.
    [J]. EDUCATION SCIENCES, 2022, 12 (12):
  • [5] AN APPROACH TO TEACHING ARTIFICIAL INTELLIGENCE IN SCHOOL
    Glushkova, Todorka
    Stoyanov, Stanimir
    Tabakova-Komsalova, Veneta
    Grancharova-Hristova, Maria
    Krasteva, Irina
    [J]. INNOVATIVE EDUCATIONAL TECHNOLOGIES, TOOLS AND METHODS FOR E-LEARNING, 2020, 12 : 257 - 267
  • [6] Explainable artificial intelligence
    Wickramasinghe, Chathurika S.
    Marino, Daniel
    Amarasinghe, Kasun
    [J]. FRONTIERS IN COMPUTER SCIENCE, 2023, 5
  • [7] Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
    Scott Cheng-Hsin Yang
    Wai Keen Vong
    Ravi B. Sojitra
    Tomas Folke
    Patrick Shafto
    [J]. Scientific Reports, 11
  • [8] Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
    Yang, Scott Cheng-Hsin
    Vong, Wai Keen
    Sojitra, Ravi B.
    Folke, Tomas
    Shafto, Patrick
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)
  • [9] Teaching artificial intelligence to nursing and midwifery students
    O'Connor, Siobhan
    [J]. NURSE EDUCATION IN PRACTICE, 2022, 64
  • [10] Explainable Artificial Intelligence for Kids
    Alonso, Jose M.
    [J]. PROCEEDINGS OF THE 11TH CONFERENCE OF THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY (EUSFLAT 2019), 2019, 1 : 134 - 141
12345