  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.
