Cited by 98Explainable Artificial Intelligence (XAI)Data Mining Algorithms and ApplicationsMachine Learning and Data Classification

A peek into the black box: exploring classifiers by randomization

Henelius, Andreas, Puolamäki, Kai, Boström, Henrik, Asker, Lars, Papapetrou, Panagiotis·2014·Data Mining and Knowledge Discovery

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Crossref · 10.1007/s10618-014-0368-8

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36 of 36 references verified0 citation errors · 0 not checkable

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  • Blinding audit approaches explore how partially blinding features (sensitive or otherwise) affect model performance.

    “Speciically, such approaches explore how partially blinding features (sensitive or otherwise) afect model performance.”

    From Fairness in machine learning: A survey (2024) · cites this paper as [143]

  • Blinding (or partial blinding) has been used as a fairness audit mechanism.

    “Blinding (or partial blinding) has also been used as a fairness audit mechanism [2,82,143].”

    From Fairness in machine learning: A survey (2024) · cites this paper as [143]

  • Causal methods can provide visual descriptions of (un)fairness in the dataset.

    “Causal methods can provide visual descriptions of (un)fairness in the dataset (see [143,173,182,183]).”

    From Fairness in machine learning: A survey (2024) · cites this paper as [143]

  • Causal methods are specifically well suited to providing transparency with respect to how classification decisions were made.

    “Thus, causal methods are speciically well suited to identifying 1) proxies of sensitive variables as discussed in subsection 2.1; 2) which subgroups are most (un)fairly treated and diferentiate the types of bias exhibited [118]; and 3) provide transparency with respect to how (classiication) decisions were made [143].”

    From Fairness in machine learning: A survey (2024) · cites this paper as [143]

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Authors
Henelius, A., Puolamäki, K., Boström, H., Asker, L., & Papapetrou, P.
Affiliations
  • Finnish Institute of Occupational Health (FI)
  • Stockholm University (SE)
Corresponding author
Henelius, Andreas
Venue
Data Mining and Knowledge Discovery
Published
2014-07-23
Volume
28
Issue
5-6
Pages
1503-1529
ISSN
  • 1384-5810 (ISSN-L)
  • 1573-756X
OpenAlex
W2090026354
Cited by
98
References
35
Citations by year
2026: 1 · 2025: 3 · 2024: 8 · 2023: 11 · 2022: 17 · 2021: 21 · 2020: 15 · 2019: 8 · 2018: 5 · 2017: 1 · 2016: 5 · 2015: 2 · 2014: 1
FWCI
2.99
Type
journal article
Language
en
Open access
closed
Locations
Data Mining and Knowledge Discovery (publishedVersion)
Funders
  • Tekes
  • Stockholms Universitet
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
classifier interpretability, feature importance, attribute interaction, randomization, black-box classifiers, classifier comparison, feature selection

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