Toward Ethical Facial Age Estimation: A Generalized Zero-Shot Benchmark Without Training on Children's Data
arXiv:2605.29230v1 Announce Type: cross Abstract: Age estimation from facial images typically relies on training data that includes images of minors, a practice that raises serious ethical, legal, and privacy concerns. In this work, we propose a generalized zero-shot benchmark for facial age estimation that explicitly excludes children's data during training while still assessing model performance on younger populations. We revisit six widely used datasets and introduce standardized splits with strict age-group separation: samples aged 18-59 for training, validation, and testing; samples under
The increasing scrutiny on AI ethics, particularly concerning data privacy and the protection of minors, is pushing researchers to develop more responsible methodologies for applications like facial age estimation.
This work directly addresses critical ethical and legal concerns surrounding AI development and deployment, which can impact regulatory frameworks and public acceptance of facial recognition technologies.
The proposed benchmark allows for the development and evaluation of facial age estimation models without relying on sensitive data from minors, setting a new standard for ethical AI practices.
- · AI ethicists
- · Privacy advocates
- · Responsible AI developers
- · Regulators
- · Developers ignoring ethical data practices
- · Companies relying on broad data collection
New benchmarks and methodologies will emerge for developing ethical AI, particularly in sensitive areas like biometrics.
Increased trust in AI systems that demonstrate adherence to ethical guidelines, potentially accelerating wider adoption in regulated industries.
The establishment of global standards for 'ethical-by-design' AI, driving a shift in how AI models are trained and deployed across various applications.
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Read at arXiv cs.AI