283 research outputs found
Fair Attribute Completion on Graph with Missing Attributes
Tackling unfairness in graph learning models is a challenging task, as the
unfairness issues on graphs involve both attributes and topological structures.
Existing work on fair graph learning simply assumes that attributes of all
nodes are available for model training and then makes fair predictions. In
practice, however, the attributes of some nodes might not be accessible due to
missing data or privacy concerns, which makes fair graph learning even more
challenging. In this paper, we propose FairAC, a fair attribute completion
method, to complement missing information and learn fair node embeddings for
graphs with missing attributes. FairAC adopts an attention mechanism to deal
with the attribute missing problem and meanwhile, it mitigates two types of
unfairness, i.e., feature unfairness from attributes and topological unfairness
due to attribute completion. FairAC can work on various types of homogeneous
graphs and generate fair embeddings for them and thus can be applied to most
downstream tasks to improve their fairness performance. To our best knowledge,
FairAC is the first method that jointly addresses the graph attribution
completion and graph unfairness problems. Experimental results on benchmark
datasets show that our method achieves better fairness performance with less
sacrifice in accuracy, compared with the state-of-the-art methods of fair graph
learning. Code is available at: https://github.com/donglgcn/FairAC
Towards Explainable Visual Anomaly Detection
Anomaly detection and localization of visual data, including images and
videos, are of great significance in both machine learning academia and applied
real-world scenarios. Despite the rapid development of visual anomaly detection
techniques in recent years, the interpretations of these black-box models and
reasonable explanations of why anomalies can be distinguished out are scarce.
This paper provides the first survey concentrated on explainable visual anomaly
detection methods. We first introduce the basic background of image-level
anomaly detection and video-level anomaly detection, followed by the current
explainable approaches for visual anomaly detection. Then, as the main content
of this survey, a comprehensive and exhaustive literature review of explainable
anomaly detection methods for both images and videos is presented. Finally, we
discuss several promising future directions and open problems to explore on the
explainability of visual anomaly detection
Trustworthy Representation Learning Across Domains
As AI systems have obtained significant performance to be deployed widely in
our daily live and human society, people both enjoy the benefits brought by
these technologies and suffer many social issues induced by these systems. To
make AI systems good enough and trustworthy, plenty of researches have been
done to build guidelines for trustworthy AI systems. Machine learning is one of
the most important parts for AI systems and representation learning is the
fundamental technology in machine learning. How to make the representation
learning trustworthy in real-world application, e.g., cross domain scenarios,
is very valuable and necessary for both machine learning and AI system fields.
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy
representation learning across domains framework which includes four concepts,
i.e, robustness, privacy, fairness, and explainability, to give a comprehensive
literature review on this research direction. Specifically, we first introduce
the details of the proposed trustworthy framework for representation learning
across domains. Second, we provide basic notions and comprehensively summarize
existing methods for the trustworthy framework from four concepts. Finally, we
conclude this survey with insights and discussions on future research
directions.Comment: 38 pages, 15 figure
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