The field of mobile, wearable, and ubiquitous computing (UbiComp) 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 are not immune to biases with respect to sensitive attributes (e.g., gender, race), leading to discriminatory outcomes. The research communities of HCI and AI-Ethics have recently started to explore ways of reporting information about datasets to surface and, eventually, counter those biases. The goal of this work is to explore the extent to which the UbiComp community has adopted such ways of reporting and highlight potential shortcomings. Through a systematic review of papers published in the Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) journal over the past 5 years (2018-2022), we found that progress on algorithmic fairness within the UbiComp community lags behind. 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. In light of these findings, our work provides practical guidelines for the design and development of ubiquitous technologies that not only strive for accuracy but also for fairness.

翻译：移动、可穿戴与普适计算（UbiComp）领域正经历机器学习的革命性融合。设备现可诊断疾病、预测心律异常，并释放人类认知的全部潜能。然而，底层算法在涉及敏感属性（如性别、种族）时并非免疫于偏见，从而导致歧视性结果。人机交互与人工智能伦理研究界最近已开始探索记录数据集信息的方法，以揭示并最终消除这些偏见。本研究旨在探究UbiComp社区在多大程度上采用了此类记录方法，并揭示潜在不足。通过对《ACM交互、移动、可穿戴与普适技术汇刊》（IMWUT）近五年（2018-2022）发表论文的系统性综述，我们发现UbiComp社区在算法公平性方面的进展滞后。研究结果表明，仅少量（5%）已发表论文遵循现代公平性报告规范，而绝大多数论文仍侧重于准确率或误差指标。基于此，本工作为设计与开发既追求准确率亦追求公平性的普适技术提供了实践指南。