Towards a Quantitative Evaluation Framework for Trustworthy AI in Facial Analysis

Simple item page
dc.contributor.authorSchreiner, Annika
dc.contributor.authorKemmerzell, Nils
dc.date.accessioned2023-12-26T18:55:48Z
dc.date.available2023-12-26T18:55:48Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.937
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other1d76aa7b-f80a-40ba-9bce-971b6e583a7a
dc.identifier.urihttps://hdl.handle.net/10125/107326
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTrustworthy Artificial Intelligence and Machine Learning
dc.subjectevaluation
dc.subjectfacial analysis
dc.subjecttrustworthy ai
dc.titleTowards a Quantitative Evaluation Framework for Trustworthy AI in Facial Analysis
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractAs machine learning (ML) models are increasingly being used in real-life applications, ensuring their trustworthiness has become a rising concern. Previous research has extensively examined individual perspectives on trustworthiness, such as fairness, robustness, privacy, and explainability. Investigating their interrelations could be the next step in achieving an improved understanding of the trustworthiness of ML models. By conducting experiments within the context of facial analysis, we explore the feasibility of quantifying multiple aspects of trustworthiness within a unified evaluation framework. Our results indicate the viability of such a framework, achieved through the aggregation of diverse metrics into holistic scores. This framework can serve as a practical tool to assess ML models in terms of multiple aspects of trustworthiness, specifically enabling the quantification of their interactions and the impact of training data. Finally, we discuss potential solutions to key technical challenges in developing the framework and the opportunities of its transfer to other use cases.
dcterms.extent10 pages
prism.startingpage7821

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0763.pdf
Size:
205.6 KB
Format:
Adobe Portable Document Format
Download

Collections

Trustworthy Artificial Intelligence and Machine Learning