The coming of age of interpretable and explainable machine learning models

被引：40
|
作者：
Lisboa, P. J. G. [1 ]
Saralajew, S. [2 ]
Vellido, A. [3 ,4 ]
Fernandez-Domenech, R. [3 ,4 ]
Villmann, T. [5 ]
机构：
[1] Liverpool John Moores Univ, Liverpool, England
[2] NEC Labs Europe GmbH, Heidelberg, Germany
[3] UPC BarcelonaTech, Dept Comp Sci, Barcelona, Spain
[4] UPC Res Ctr, IDEAI, Barcelona, Spain
[5] Univ Appl Sci Mittweida, Saxon Inst Comp Intelligence & Machine Learning, Mittweida, Germany
来源：
NEUROCOMPUTING | 2023年 / 535卷
关键词：
XAI; Interpretable ML; Explainable ML; Transparent AI; AUTOMATED DECISION-MAKING; NEURAL-NETWORKS; ARTIFICIAL-INTELLIGENCE; CLASSIFICATION; EXPLANATION;
D O I：
10.1016/j.neucom.2023.02.040
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Machine-learning-based systems are now part of a wide array of real-world applications seamlessly embedded in the social realm. In the wake of this realization, strict legal regulations for these systems are currently being developed, addressing some of the risks they may pose. This is the coming of age of the concepts of interpretability and explainability in machine-learning-based data analysis, which can no longer be seen just as an academic research problem. In this paper, we discuss explainable and interpretable machine learning as post hoc and ante-hoc strategies to address regulatory restrictions and highlight several aspects related to them, including their evaluation and assessment and the legal boundaries of application.(c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码：25 / 39
页数：15
相关论文
共 50 条
  • [21] An interpretable schizophrenia diagnosis framework using machine learning and explainable artificial intelligence
    Shivaprasad, Samhita
    Chadaga, Krishnaraj
    Dias, Cifha Crecil
    Sampathila, Niranjana
    Prabhu, Srikanth
    SYSTEMS SCIENCE & CONTROL ENGINEERING, 2024, 12 (01)
  • [22] Active Sampling for Learning Interpretable Surrogate Machine Learning Models
    Saadallah, Amal
    Morik, Katharina
    2020 IEEE 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2020), 2020, : 264 - 272
  • [23] Interpretable Pneumonia Detection by Combining Deep Learning and Explainable Models With Multisource Data
    Ren, Hao
    Wong, Aslan B.
    Lian, Wanmin
    Cheng, Weibin
    Zhang, Ying
    He, Jianwei
    Liu, Qingfeng
    Yang, Jiasheng
    Zhang, Chen Jason
    Wu, Kaishun
    Zhang, Haodi
    IEEE ACCESS, 2021, 9 : 95872 - 95883
  • [24] Interpretable Machine Learning Using Partial Linear Models
    Flachaire, Emmanuel
    Hue, Sullivan
    Laurent, Sebastien
    Hacheme, Gilles
    OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2024, 86 (03) : 519 - 540
  • [25] Toward Interpretable Machine Learning Models for Materials Discovery
    Mikulskis, Paulius
    Alexander, Morgan R.
    Winkler, David Alan
    ADVANCED INTELLIGENT SYSTEMS, 2019, 1 (08)
  • [26] Interpretable Machine Learning Models for PISA Results in Mathematics
    Gomez-Talal, Ismael
    Bote-Curiel, Luis
    Luis Rojo-Alvarez, Jose
    IEEE ACCESS, 2025, 13 : 27371 - 27397
  • [27] Explainable Machine Learning for Lung Cancer Screening Models
    Kobylinska, Katarzyna
    Orlowski, Tadeusz
    Adamek, Mariusz
    Biecek, Przemyslaw
    APPLIED SCIENCES-BASEL, 2022, 12 (04):
  • [28] Explainable Machine Learning Models Assessing Lending Risk
    Nassiri, Khalid
    Akhloufi, Moulay A.
    NAVIGATING THE TECHNOLOGICAL TIDE: THE EVOLUTION AND CHALLENGES OF BUSINESS MODEL INNOVATION, VOL 3, ICBT 2024, 2024, 1082 : 519 - 529
  • [29] Application of interpretable machine learning models for the intelligent decision
    Li, Yawen
    Yang, Liu
    Yang, Bohan
    Wang, Ning
    Wu, Tian
    NEUROCOMPUTING, 2019, 333 : 273 - 283
  • [30] Explainable machine learning models to analyse maternal health
    Patel, Shivshanker Singh
    DATA & KNOWLEDGE ENGINEERING, 2023, 146
12345