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 with do-no-harm REgularization, a demographic- and heterogeneous-attribute-agnostic framework that aligns human-centered fairness with ethical principles for ubiquitous and mobile sensing. Flare leverages optimization geometry, particularly Fisher Information, to regularize curvature and uncover latent disparities in model behavior without demographic or heterogeneous attributes. By integrating representation, loss, and curvature signals, it identifies hidden performance strata and refines them through collaborative but do-no-harm optimization, enhancing subgroup performance while preserving ethical balance. We also introduce BHE (Beneficence-Harm Avoidance-Equity), a metric suite that operationalizes ethical fairness beyond statistical parity. Across mobile physiological, behavioral, and clinical sensing datasets, including EDA, OhioT1DM, IHS, and Percept-R, Flare improves ethical fairness over state-of-the-art baselines. Ablation, interpretability, and loss-landscape analyses show that these gains arise from flatter optimization geometry, simpler decision rules, and do-no-harm latent-subgroup adaptation. Runtime analysis supports the practicality of Flare for resource-constrained sensing deployments.

翻译：在普适与移动健康系统中，计算模型基于可穿戴、行为及生理传感数据推断人类状态。在这些场景中，仅追求高准确率远远不够；模型必须跨不同人群、场景及设备以符合伦理且公平的方式运行。然而，依赖人口统计学或异构属性进行训练的公平性方法难以实施，因为此类属性常不可获取、涉及隐私敏感、受到监管或不宜收集。传统的基于均等性的公平性方法也可能因牺牲子组性能而违背伦理原则。针对这一挑战，我们提出Flare（Fisher引导的潜在子组学习与无害化正则化），一种无需人口统计学及异构属性的框架，将以人为本的公平性与普适移动传感的伦理原则对齐。Flare利用优化几何（特别是Fisher信息）来正则化曲率，并揭示模型行为中的潜在差异，而无需人口统计学或异构属性。通过整合表征、损失及曲率信号，它识别隐藏的性能层次，并通过协同但无害化的优化对其进行精炼，在提升子组性能的同时维持伦理平衡。我们还引入BHE（善行-避害-公平性）指标套件，将伦理公平性从统计均等性层面提升至操作化实现。在涵盖EDA、OhioT1DM、IHS及Percept-R的移动生理、行为及临床传感数据集中，Flare相较于现有最优基线提升了伦理公平性。消融实验、可解释性及损失景观分析表明，这些改进源于更平坦的优化几何、更简单的决策规则以及无害化的潜在子组自适应。运行时分析支持Flare在资源受限的传感部署中的实用性。