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Trustworthy AI under Imperfect Web Data
Jiangchao Yao
, Feng Liu
,
Bo Han
Department of Computer Science
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
1
Citation (Scopus)
Overview
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Dive into the research topics of 'Trustworthy AI under Imperfect Web Data'. Together they form a unique fingerprint.
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Keyphrases
Adversarial Data
33%
Algorithm Design
33%
Balanced Data
33%
Building Trust
33%
Clean Data
33%
Data Harms
33%
Data with Imperfect Labels
33%
Dataset Size
33%
Existing Algorithms
33%
Image Data
33%
Imperfect Data
100%
Labelled Image
33%
Learning Algorithm
33%
Learning Methods
33%
Long Tail
33%
Long-tailed Data
33%
Misinformation
33%
Noisy Data
33%
Online Data Collection
33%
Personalization
33%
Speech Data
33%
Supervised Information
33%
Trustworthy AI
100%
Unethical Practices
33%
User Confidence
33%
User Data Privacy
33%
User Engagement
33%
User Satisfaction
33%
Web Application
100%
Web of Data
100%
Web-scale
33%
Computer Science
Artificial Intelligence
33%
Data Privacy
33%
Decision-Making
33%
Enhance Security
33%
Learning Algorithm
33%
Misinformation
33%
Supervised Information
33%
Trustworthy AI
100%
User Data
33%
User Engagement
33%
User Satisfaction
33%
Web Application
100%
Engineering
Artificial Intelligence
100%
Data Privacy
25%
Image Data
25%
Learning Algorithm
25%
Noisy Data
25%
Scale Image
25%
Speech Data
25%
Mathematics
Balanced Data
33%
Distributed Data
33%
Noisy Data
33%
Trustworthy AI
100%