Tracing the bias loop: AI, cultural heritage and bias-mitigating in practice

This article investigates the pervasive issue of bias within AI-driven cultural heritage collections, emphasizing how digital technologies both inherit and amplify existing societal and historical prejudices embedded in analogue records. It outlines the multifaceted nature of bias—ranging from data...

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Veröffentlicht in:AI & society Jg. 40; H. 8; S. 5835 - 5847
Hauptverfasser: Foka, Anna, Griffin, Gabriele, Ortiz Pablo, Dalia, Rajkowska, Paulina, Badri, Sushruth
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.12.2025
Springer Nature B.V
Schlagworte:
Algorithms
Annotations
Art galleries & museums
Artificial Intelligence
Bias
Bias mitigation
Biblioteks- och informationsvetenskap
Classification
Computer Science
Computer vision
Computerized Image Processing
Control
Cultural heritage
Cultural resources
Data augmentation
Data collection
Datasets
Datoriserad bildbehandling
Digital curation
Digital technology
Digitization
Engineering Economics
Image classification
Information Studies
Interdisciplinarycollaboration
Logistics
Machine learning
Marketing
Mechatronics
Metadata
Methodology of the Social Sciences
Organization
Performing Arts
Prejudice
Representations
Robotics
Machine learning
Cultural heritage
Bias mitigation
Data augmentation
Interdisciplinarycollaboration
Artificial intelligence
Image classification
ISSN:0951-5666, 1435-5655, 1435-5655
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This article investigates the pervasive issue of bias within AI-driven cultural heritage collections, emphasizing how digital technologies both inherit and amplify existing societal and historical prejudices embedded in analogue records. It outlines the multifaceted nature of bias—ranging from data selection and annotation to algorithmic design and user interaction—demonstrating how each stage of the AI pipeline can introduce or perpetuate distortions in representation. Through a critical review of current scholarship and practical case studies, particularly in image classification, the article evaluates technical strategies such as data augmentation, adversarial debiasing, and monitoring plans for bias mitigation. The findings reveal that while methods like noise injection and colour jittering can balance datasets and improve model fairness, effective bias mitigation ultimately depends on interdisciplinary collaboration between heritage professionals, subject experts, and data scientists. The article concludes that addressing bias requires an ongoing, holistic approach, integrating both technical and humanistic perspectives from data collection to model deployment. This ensures more inclusive, accurate, and ethically sound representations of cultural heritage, supporting the sector’s goals of diversity and accessibility for future audiences.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0951-5666
1435-5655
1435-5655
DOI:10.1007/s00146-025-02349-z