@inproceedings{Yfantidou2023FairComp,
	title        = {FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing},
	author       = {Yfantidou, Sofia and Spathis, Dimitris and Constantinides, Marios and Xia, Tong and van Berkel, Niels},
	year         = 2023,
	booktitle    = {Adjunct Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing},
	location     = {UbiComp'23 Adj.},
	pages        = {to appear},
	abstract     = {How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are both ethical and fair? While fairness in machine learning (ML) has gained traction in recent years, fairness in UbiComp remains unexplored. This workshop aims to discuss fairness in UbiComp research and its social, technical, and legal implications. From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights. From a technical perspective, we will initiate a discussion on data collection and modeling practices to develop bias mitigation approaches tailored to UbiComp research. From a legal perspective, we will examine how new policies and regulations shape our community's work and future research. We aim to foster a vibrant community centered around the topic of responsible UbiComp, while also charting a clear path for future research endeavours in this field.},
	type         = {Conference Paper},
}