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Testimonial injustice in medical machine learning
Giorgia Pozzi
Ethics & Philosophy of Technology
Research output
:
Contribution to journal
›
Article
›
Scientific
›
peer-review
19
Citations (Scopus)
43
Downloads (Pure)
Overview
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Arts and Humanities
Testimonial Injustice
100%
Medical
100%
Machines
100%
Patients
60%
System
60%
Credibility
40%
Social Inequality
10%
marker's
10%
Silence
10%
Program
10%
Health Care
10%
Encounter
10%
Moral Justification
10%
Application
10%
Medicine
10%
Analysis
10%
Prediction
10%
Epistemic
10%
Risk
10%
Cure
10%
Trustworthiness
10%
Social Group
10%
Loci
10%
Clinical
10%
Epistemic Justification
10%
USA
10%
Dimension
10%
Social Sciences
Learning
100%
Machines
100%
Patients
50%
Credibility
40%
Trustworthiness
10%
Social Class
10%
USA
10%
Social Inequality
10%
Morality
10%
Forecasting
10%
Medical Sciences
10%
Analysis
10%
Program
10%
Risk
10%
Drugs
10%
Application
10%
Health Care
10%
Physician-Patient Relations
10%
Opinion
10%
Responsibility
10%
INIS
machine learning
100%
patients
60%
prediction
10%
bears
10%
applications
10%
risks
10%
dimensions
10%
drugs
10%
monitoring
10%
usa
10%
medicine
10%
Pharmacology, Toxicology and Pharmaceutical Science
2 Decanoylamino 3 Morpholino 1 Phenyl 1 Propanol
100%
Therapeutic Drug Monitoring
50%
Opiate
50%
Adverse Outcome
50%
Keyphrases
Doctor-patient Relationship
20%
Vulnerable Social Groups
20%
Epistemic Justification
20%
Patient Voice
20%
Safe Machine Learning
20%
Medical Encounter
20%
Medicine and Dentistry
2 Decanoylamino 3 Morpholino 1 Phenyl 1 Propanol
20%
Therapeutic Drug Monitoring
10%
Doctor Patient Relation
10%