mlsanityData to 7 Oct 2026

FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing

arXiv 2309.12877 ↗First posted 22 Sep 2023cs.CY cs.ET cs.LGcited by 0

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Abstract (arXiv)

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 practices to develop bias mitigation approaches tailored to UbiComp research. From a legal perspective, we will examine how new policies 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 22 Sep 2023.

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9 of 38 references.

References · 9

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2021A survey on datasets for fairness-aware machine learningcited by 157
  3. 2020Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Datacited by 60
  4. 2023WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?cited by 28
  5. 2020FairLens: Auditing Black-box Clinical Decision Support Systemscited by 23
  6. 2023Human-Centered Responsible Artificial Intelligence: Current & Future Trendscited by 8
  7. 2023(Un)fair devices: Moving beyond AI accuracy in personal sensingcited by 6
  8. 2022Good Intentions, Bad Inventions: How Employees Judge Pervasive Technologies in the Workplacecited by 4
  9. 2018Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Beingcited by 3

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