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Cited by 29Recommender Systems and TechniquesEthics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems

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Misztal-Radecka, Joanna, Indurkhya, Bipin·2021·Information Processing & Management
DOI 10.1016/j.ipm.2021.102519
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  • The original paper does not outline a detailed methodology for testing whether there is a statistically significant difference in the bias metric between the clusters identified by the HBAC algorithm.

    “In the original paper, no detailed methodology is outlined for testing whether there is a statistically significant difference in the bias metric between the clusters identified by the HBAC algorithm.”

    From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Misztal-Radecka and Indurkhya, 2021

    Section 2 (Proposed approach; page unavailable) specifies a significance test for bias differences: it compares each cluster with the remaining users using Welch’s t-test and identifies significant differences at a 95% confidence level. This is not a procedure for every pairwise cluster comparison, but it contradicts the claim that the paper outlines no methodology for significance testing of detected clusters’ bias. (LLM, conf=0.86)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • HBAC is an iterative clustering algorithm that produces a partition of the original dataset to isolate potentially unfairly treated groups into distinct clusters.

    “HBAC is an iterative clustering algorithm that produces a partition of the original dataset to isolate potentially unfairly treated groups into distinct clusters.”

    From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Misztal-Radecka and Indurkhya, 2021

    Section 2.1.2 describes an iterative clustering method that splits user clusters when doing so reveals greater bias, matching the claimed purpose of separating potentially unfairly treated groups. The paper calls the method BAH-KM, not HBAC, so it does not support the algorithm name used in the claim. No page number is available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Unsupervised learning can aid in detecting groups in the data for which a given notion of equal treatment is violated, avoiding the need for ex-ante specification of demographic or legally protected groups.

    “Unsupervised learning can aid in this challenge by detecting groups in the data for which a given notion of equal treatment is violated, avoiding the need for ex-ante specification of demographic or legally protected groups (Misztal-Radecka and Indurkhya, 2021).”

    From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Misztal-Radecka and Indurkhya, 2021

  • The original algorithm does not specify how to assign clusters to data points that are not part of the original training dataset.

    “The original algorithm from Misztal-Radecka and Indurkhya (2021) does not specify how to assign clusters to data points that are not part of the original training dataset.”

    From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Misztal-Radecka and Indurkhya, 2021

  • The simulation study identifies three separate issues with the original implementation by Misztal-Radecka and Indurkhya (2021).

    “In the simulation study, we identify three separate issues with the original implementation by MisztalRadecka and Indurkhya (2021) that we argued for in Section 3.”

    From Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool (2025) · cites this paper as Misztal-Radecka and Indurkhya (2021)

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References 78 · Cited by 29

Research works printed but not linked: 49

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Authors
Misztal-Radecka, J., & Indurkhya, B.
Affiliations
  • AGH University of Krakow (PL)
  • Jagiellonian University (PL)
Corresponding author
Misztal-Radecka, Joanna
ORCID
Venue
Information Processing & Management
Publisher
Elsevier BV
Published
2021-05
Volume
58
Issue
3
Pages
102519
ISSN
  • 0020-0271 (ISSN-L)
  • 0306-4573
DOI
10.1016/j.ipm.2021.102519
OpenAlex
W3126854025
Cited by
29
References
78
Citations by year
2026: 8 · 2025: 2 · 2024: 7 · 2023: 5 · 2022: 5 · 2021: 2
FWCI
3.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Processing & Management (publishedVersion)
  • Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
Funders
Ministerstwo Nauki i Szkolnictwa Wyższego
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
algorithmic discrimination, recommender systems, customer segmentation, post-hoc explanation, recommendation diversity, latent space embedding

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