arXiv:2603.13373v3 Announce Type: replace-cross Abstract: In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data. In these settings, high accuracy alone is insufficient; models must act ethically and equitably across diverse people, contexts, and devices. However, fairness methods that rely on demographic or heterogeneous attributes during training are difficult to enforce because such attributes are often unavailable, privacy-sensitive, regulated, or undesirable to collect. Conventional parity-based fairness c
The proliferation of AI in sensitive applications like health is forcing a re-evaluation of ethical AI design, especially as regulatory scrutiny on data privacy intensifies.
This research addresses a critical limitation in current ethical AI frameworks by proposing demographic-agnostic fairness, making AI deployment more feasible and compliant in privacy-sensitive sectors.
AI models can potentially achieve ethical fairness without relying on sensitive demographic data, reducing privacy risks and making equitable AI more widely applicable.
- · AI developers in healthcare
- · Privacy-focused AI companies
- · Users of health monitoring systems
- · AI ethics research
- · AI systems relying solely on demographic data
- · Developers ignoring privacy-preserving design
Increased adoption of AI in regulated and privacy-conscious sectors like healthcare and finance.
New standards and regulations may emerge focusing on demographic-agnostic fairness metrics and methods.
Reduced 'AI ethics washing' as verifiable, privacy-preserving fairness becomes a design requirement rather than an afterthought.
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Read at arXiv cs.LG