Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323620 
Year of Publication: 
2025
Citation: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 35 [Issue:] 1 [Article No.:] 24 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Artificial intelligence (AI) systems create value but can pose substantial risks, particularly due to their black-box nature and potential bias towards certain individuals. In response, recent legal initiatives require organizations to ensure their AI systems conform to overarching principles such as explainability and fairness. However, conducting such conformity assessments poses significant challenges for organizations, including a lack of skilled experts and ambiguous guidelines. In this paper, the authors help organizations by providing a design framework for assessing the conformity of AI systems. Specifically, building upon design science research, the authors conduct expert interviews, derive design requirements and principles, instantiate the framework in an illustrative software artifact, and evaluate it in five focus group sessions. The artifact is designed to both enable a fast, semi-automated assessment of principles such as fairness and explainability and facilitate communication between AI owners and third-party stakeholders (e.g., regulators). The authors provide researchers and practitioners with insights from interviews along with design knowledge for AI conformity assessments, which may prove particularly valuable in light of upcoming regulations such as the European Union AI Act.
Subjects: 
Machine learning
Algorithmic fairness
Explainable AI
Certification
AI auditing
Impact assessment
JEL: 
M15
L86
O30
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.