- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1572-8439
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Enabling Fairness in Healthcare Through Machine Learning ; volume:24 ; number:3 ; day:31 ; month:8 ; year:2022 ; pages:1-13 ; date:9.2022
Ethics and information technology ; 24, Heft 3 (31.8.2022), 1-13, 9.2022
- Urheber
-
Grote, Thomas
Keeling, Geoff
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s10676-022-09658-7
- URN
-
urn:nbn:de:101:1-2022111821022688940162
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:29 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Grote, Thomas
- Keeling, Geoff
- SpringerLink (Online service)
Ähnliche Objekte (12)
Generic sensor framework enabling personalized healthcare
Enabling inter-organizational analytics in business networks through meta machine learning
Limitations of Fairness in Machine Learning
Operationalizing fairness for responsible machine learning
Enabling reciprocity through blockchain design
Empowering advanced medical decision-making through machine learning in healthcare
On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare
Fairness of artificial intelligence in healthcare: review and recommendations
Enabling Smart Parking Services with Machine Learning
Fairness-aware Machine Learning in Educational Data Mining
Enabling retroactive computing through event sourcing
Enabling electroweak baryogenesis through dark matter
Generic sensor framework enabling personalized healthcare
Enabling inter-organizational analytics in business networks through meta machine learning
Limitations of Fairness in Machine Learning
Operationalizing fairness for responsible machine learning
Enabling reciprocity through blockchain design
Empowering advanced medical decision-making through machine learning in healthcare
On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare
Fairness of artificial intelligence in healthcare: review and recommendations
Enabling Smart Parking Services with Machine Learning
Fairness-aware Machine Learning in Educational Data Mining
Enabling retroactive computing through event sourcing
Enabling electroweak baryogenesis through dark matter
Generic sensor framework enabling personalized healthcare
Enabling inter-organizational analytics in business networks through meta machine learning
Limitations of Fairness in Machine Learning
Operationalizing fairness for responsible machine learning
Enabling reciprocity through blockchain design
Empowering advanced medical decision-making through machine learning in healthcare
On responsible machine learning datasets emphasizing fairness, privacy and regulatory norms with examples in biometrics and healthcare
Fairness of artificial intelligence in healthcare: review and recommendations
Enabling Smart Parking Services with Machine Learning
Fairness-aware Machine Learning in Educational Data Mining
Enabling retroactive computing through event sourcing