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
  • [31] Deep Learning Explainability with Local Interpretable Model-Agnostic Explanations for Monkeypox Prediction
    Angmo, Motup
    Sharma, Nonita
    Mohanty, Sachi Nandan
    Ijaz Khan, M.
    Mamatov, Abdugafur
    Kallel, Mohamed
    JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY, 2025,
  • [32] A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability
    Bomrah, Sherali
    Uddin, Mohy
    Upadhyay, Umashankar
    Komorowski, Matthieu
    Priya, Jyoti
    Dhar, Eshita
    Hsu, Shih-Chang
    Syed-Abdul, Shabbir
    CRITICAL CARE, 2024, 28 (01)
  • [33] Machine learning explainability via microaggregation and shallow decision trees
    Blanco-Justicia, Alberto
    Domingo-Ferrer, Josep
    Martinez, Sergio
    Sanchez, David
    KNOWLEDGE-BASED SYSTEMS, 2020, 194
  • [34] Improving the performance of the intrusion detection systems by the machine learning explainability
    Quang-Vinh Dang
    INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS, 2021, 17 (05) : 537 - 555
  • [35] Explainability-based Debugging of Machine Learning for Vulnerability Discovery
    Sotgiu, Angelo
    Pintor, Maura
    Biggio, Battista
    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, ARES 2022, 2022,
  • [36] FairXAI -A Taxonomy and Framework for Fairness and Explainability Synergy in Machine Learning
    Ramachandranpillai, Resmi
    Baeza-Yates, Ricardo
    Heintz, Fredrik
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2025,
  • [37] Accuracy and explainability of statistical and machine learning xG models in football
    Cefis, Mattia
    Carpita, Maurizio
    STATISTICS, 2025, 59 (02) : 426 - 445
  • [38] Machine learning and deep analytics for biocomputing: call for better explainability
    Petkovic, Dragutin
    Kobzik, Lester
    Re, Christopher
    PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018 (PSB), 2018, : 623 - 627
  • [39] Machine Learning Explainability for Intrusion Detection in the Industrial Internet of Things
    Ahakonye L.A.C.
    Nwakanma C.I.
    Lee J.M.
    Kim D.-S.
    IEEE Internet of Things Magazine, 2024, 7 (03): : 68 - 74
  • [40] A Short Survey on Machine Learning Explainability: An Application to Periocular Recognition
    Brito, Joao
    Proenca, Hugo
    ELECTRONICS, 2021, 10 (15)
12345