Justificatory explanations: a step beyond explainability in machine learning

被引：0
|
作者：
Guersenzvaig, A. [1 ]
Casacuberta, D. [2 ]
机构：
[1] ELISAVA Barcelona, Sch Design & Engn, Barcelona, Spain
[2] Autonomous Univ Barcelona, Philosophy, Barcelona, Spain
来源：
EUROPEAN JOURNAL OF PUBLIC HEALTH | 2023年 / 33卷
关键词：
D O I：
暂无
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
摘要：
ckad160.87
引用
收藏
页数：1
相关论文
共 50 条
  • [1] Directive Explanations for Actionable Explainability in Machine Learning Applications
    Singh, Ronal
    Miller, Tim
    Lyons, Henrietta
    Sonenberg, Liz
    Velloso, Eduardo
    Vetere, Frank
    Howe, Piers
    Dourish, Paul
    ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS, 2023, 13 (04)
  • [2] A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques
    Nogueira, Adara S. R.
    Ferreira, Artur J.
    Figueiredo, Mario A. T.
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM), 2021, : 362 - 369
  • [3] Beyond model interpretability: socio-structural explanations in machine learning
    Smart, Andrew
    Kasirzadeh, Atoosa
    AI & SOCIETY, 2024,
  • [4] Legal requirements on explainability in machine learning
    Adrien Bibal
    Michael Lognoul
    Alexandre de Streel
    Benoît Frénay
    Artificial Intelligence and Law, 2021, 29 : 149 - 169
  • [5] Legal requirements on explainability in machine learning
    Bibal, Adrien
    Lognoul, Michael
    de Streel, Alexandre
    Frenay, Benoit
    ARTIFICIAL INTELLIGENCE AND LAW, 2021, 29 (02) : 149 - 169
  • [6] A Survey on the Explainability of Supervised Machine Learning
    Burkart, Nadia
    Huber, Marco F.
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2021, 70 : 245 - 317
  • [7] Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions
    Casacuberta, David
    Guersenzvaig, Ariel
    Moyano-Fernandez, Cristian
    AI & SOCIETY, 2024, 39 (01) : 279 - 293
  • [8] Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions
    David Casacuberta
    Ariel Guersenzvaig
    Cristian Moyano-Fernández
    AI & SOCIETY, 2024, 39 : 279 - 293
  • [9] Beyond Human: Deep Learning, Explainability and Representation
    Fazi, M. Beatrice
    THEORY CULTURE & SOCIETY, 2021, 38 (7-8) : 55 - 77
  • [10] Machine Learning Explainability in Breast Cancer Survival
    Jansen, Tom
    Geleijnse, Gijs
    Van Maaren, Marissa
    Hendriks, Mathijs P.
    Ten Teije, Annette
    Moncada-Torres, Arturo
    DIGITAL PERSONALIZED HEALTH AND MEDICINE, 2020, 270 : 307 - 311
12345