Gender and Participation in Open Source Software Development

Frluckaj, Hana; Dabbish, Laura; Widder, David Gray; Qiu, Huilian Sophie; Herbsleb, James D.

doi:10.1145/3555190

Cited by 10 publications

(1 citation statement)

References 73 publications

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“…Understanding researcher positionality is essential to demystifying our lens on data collection and analysis [39,49]. We situate this review paper in a Western country (REDACTED FOR REVIEW) in the 21 st century, writing as authors who primarily work as academic and industry researchers.…”

Section: Positionality Statementmentioning

confidence: 99%

Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing

Yfantidou¹,

Constantinides²,

Spathis³

et al. 2023

Preprint

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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. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Consumer health; • Computing methodologies → Artificial intelligence; • Social and professional topics → Codes of ethics.

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Section: Positionality Statementmentioning

confidence: 99%