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 collect. Conventional parity-based fairness can also violate ethical principles by trading off subgroup performance. To address this challenge, we present Flare, Fisher-guided LAtent-subgroup learning...
| Authors | Shaily Roy, Harshit Sharma, Daniel A. Adler et al. |
|---|---|
| Primary category | cs.CY |
| Published | 10 March 2026 |
| Last updated at source | 29 May 2026 |
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