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着机器学习的革命性融合。设备现已能诊断疾病、预测心律异常，并释放人类认知的全部潜能。然而，驱动这些预测的基础算法在敏感属性（如性别、种族）方面并非免疫于偏见，导致歧视性结果。本研究旨在探索移动与可穿戴计算社区在多大程度上采纳了报告数据集与模型信息的方式，以揭示并最终对抗偏见。我们对2018-2022年间发表于《ACM交互、移动、可穿戴与普适技术学报》（IMWUT）的论文进行的系统性综述表明，尽管算法公平性方面已取得进展，但仍有广阔的发展空间。我们的发现显示，仅有少数（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同类领域的其他会议，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，发现其结果与主要结论一致。基于这些发现，本研究为追求准确率兼顾公平性的移动与可穿戴技术的设计与开发提供了实践指南。