arXiv:2602.18201v2 Announce Type: replace-cross Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on high-capacity Self-Organizing Maps, we demonstrate that sensitive attributes such as age and income emerge as dominant latent axes in purely unsupervised embeddings, even when explicitly excluded from the input. On two large-scale real-world datasets (the World Values Survey across fiv
The rapid advancement and deployment of unsupervised AI models necessitate a deeper understanding of their inherent biases and the potential for unintended discrimination.
This research reveals a fundamental flaw in the assumption of neutrality in unsupervised AI, implying that even without direct input of sensitive attributes, models can replicate or amplify societal biases, impacting fairness and ethical AI development.
The understanding that unsupervised AI models are not inherently neutral regarding sensitive attributes, even when such data is withheld, demanding new approaches to bias detection and mitigation in all AI systems.
- · Ethical AI developers
- · AI auditing firms
- · Fairness-aware AI researchers
- · Developers ignoring fairness principles
- · Organizations relying on 'neutral' unsupervised AI assumptions
- · AI systems perpetuating hidden biases
Increased scrutiny and demand for new methods to detect and mitigate hidden biases in unsupervised machine learning models across all applications.
Development of regulatory frameworks and industry standards explicitly addressing the emergence of sensitive attributes in ostensibly neutral AI representations.
A fundamental re-evaluation of data privacy and anonymization techniques, as seemingly innocuous data can implicitly reveal sensitive information within AI models.
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Read at arXiv cs.LG