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Found 8 papers, 2 papers with code
Using Self-supervised Learning Can Improve Model Fairness
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels.
How Western, Educated, Industrialized, Rich, and Democratic is Social Computing Research?
However, the studies at ICWSM still predominantly examine populations from countries that are more Educated, Industrialized, and Rich in comparison to those in FAccT, with a special note on the 'Democratic' variable reflecting political freedoms and rights.
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and labels.
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
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.
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
The field of mobile and wearable computing is undergoing a revolutionary integration of machine learning.
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence.
A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI
In this paper, we present a bottom-up mapping of the current state of research at the intersection of Human-Centered AI, Ethical, and Responsible AI (HCER-AI) by thematically reviewing and analyzing 164 research papers from leading conferences in ethical, social, and human factors of AI: AIES, CHI, CSCW, and FAccT.
Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables
Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated.
