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

arXiv: https://arxiv.org/abs/2309.12877 · HTML: https://mlsanity.com/paper/2309.12877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data](https://mlsanity.com/paper/2006.05919/) (2020)
- [WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?](https://mlsanity.com/paper/2305.06415/) (2023)
- [FairLens: Auditing Black-box Clinical Decision Support Systems](https://mlsanity.com/paper/2011.04049/) (2020)
- [Human-Centered Responsible Artificial Intelligence: Current & Future Trends](https://mlsanity.com/paper/2302.08157/) (2023)
- [(Un)fair devices: Moving beyond AI accuracy in personal sensing](https://mlsanity.com/paper/2303.15585/) (2023)
- [Good Intentions, Bad Inventions: How Employees Judge Pervasive Technologies in the Workplace](https://mlsanity.com/paper/2210.06381/) (2022)
- [Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being](https://mlsanity.com/paper/1807.02472/) (2018)

## 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.