Ethical Fairness in Ubiquitous Health Sensing without Known Attributes

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 col

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ethics.ai (10 March 2026), “Ethical Fairness in Ubiquitous Health Sensing without Known Attributes,” evidence record 7439, https://ethics.ai/record/7439 (originally published by arXiv).

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