The field of mobile and wearable computing is undergoing a revolutionary integration of machine learning. Devices can now diagnose diseases, predict heart irregularities, and unlock the full potential of human cognition. However, the underlying algorithms powering these predictions are not immune to biases with respect to sensitive attributes (e.g., gender, race), leading to discriminatory outcomes. The goal of this work is to explore the extent to which the mobile and wearable computing community has adopted ways of reporting information about datasets and models to surface and, eventually, counter biases. Our systematic review of papers published in the Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal from 2018-2022 indicates that, while there has been progress made on algorithmic fairness, there is still ample room for growth. Our findings show that only a small portion (5%) of published papers adheres to modern fairness reporting, while the overwhelming majority thereof focuses on accuracy or error metrics. To generalize these results across venues of similar scope, we analyzed recent proceedings of ACM MobiCom, MobiSys, and SenSys, IEEE Pervasive, and IEEE Transactions on Mobile Computing Computing, and found no deviation from our primary result. In light of these findings, our work provides practical guidelines for the design and development of mobile and wearable technologies that not only strive for accuracy but also fairness.

翻译：移动与可穿戴计算领域正经历机器学习技术的革命性融合。设备如今能够诊断疾病、预测心律异常，并释放人类认知的全部潜能。然而，驱动这些预测的底层算法并非对敏感属性（例如性别、种族）相关的偏差免疫，进而导致歧视性结果。本研究旨在探索移动与可穿戴计算领域在多大程度上采用了报告数据集与模型信息的方法，以揭示并最终对抗偏差。我们对2018-2022年间发表在《ACM交互式、移动、可穿戴与泛在技术会议论文集》（IMWUT）上的论文进行了系统性综述，结果表明尽管算法公平性方面已取得进展，但仍存在广阔的提升空间。研究发现，仅有小部分（5%）已发表论文遵循现代公平性报告规范，而绝大多数论文仍聚焦于准确率或误差指标。为将这一结果推广至类似范畴的学术平台，我们分析了近期ACM MobiCom、MobiSys、SenSys、IEEE Pervasive及IEEE Transactions on Mobile Computing的会议论文集，发现其结果与主要发现一致。基于这些发现，本研究为移动与可穿戴技术的设计与开发提供了实践指南，倡导在追求准确率的同时，必须兼顾公平性。