- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1610-1987
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Evaluating Explainability Methods Intended for Multiple Stakeholders ; day:7 ; month:2 ; year:2021 ; pages:1-15
Künstliche Intelligenz ; (7.2.2021), 1-15
- Urheber
-
Martin, Kyle
Liret, Anne
Wiratunga, Nirmalie
Owusu, Gilbert
Kern, Mathias
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s13218-020-00702-6
- URN
-
urn:nbn:de:101:1-2021032921132375931602
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:49 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
- Martin, Kyle
- Liret, Anne
- Wiratunga, Nirmalie
- Owusu, Gilbert
- Kern, Mathias
- SpringerLink (Online service)
Ähnliche Objekte (12)
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
RELAX: Representation Learning Explainability
Explainability of operating systems
Evaluating of feasibility and aiding explainability of scaling policies using architectural-based simulations
On Explainability and its Characterization
Explainability in Deep Reinforcement Learning
A Means to what End? Evaluating the Explainability of Software Systems using Goal-Oriented Heuristics
Can explainability improve trustworthiness in AI?
Advancing Model Explainability in Pervasive Computing
Evaluation of explainability in autoscaling frameworks
Stakeholders
Explainability, Public Reason, and Medical Artificial Intelligence
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
RELAX: Representation Learning Explainability
Explainability of operating systems
Evaluating of feasibility and aiding explainability of scaling policies using architectural-based simulations
On Explainability and its Characterization
Explainability in Deep Reinforcement Learning
A Means to what End? Evaluating the Explainability of Software Systems using Goal-Oriented Heuristics
Can explainability improve trustworthiness in AI?
Advancing Model Explainability in Pervasive Computing
Evaluation of explainability in autoscaling frameworks
Stakeholders
Explainability, Public Reason, and Medical Artificial Intelligence
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
RELAX: Representation Learning Explainability
Explainability of operating systems
Evaluating of feasibility and aiding explainability of scaling policies using architectural-based simulations
On Explainability and its Characterization
Explainability in Deep Reinforcement Learning
A Means to what End? Evaluating the Explainability of Software Systems using Goal-Oriented Heuristics
Can explainability improve trustworthiness in AI?
Advancing Model Explainability in Pervasive Computing
Evaluation of explainability in autoscaling frameworks
Stakeholders