Navigating AI conformity: A design framework to assess fairness, explainability, and performance

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Publicat a:Electronic Markets vol. 35, no. 1 (Dec 2025), p. 24
Autor principal: von Zahn, Moritz
Altres autors: Zacharias, Jan, Lowin, Maximilian, Chen, Johannes, Hinz, Oliver
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Springer Nature B.V.
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100 1 |a von Zahn, Moritz  |u Goethe University, Information Systems and Information Management, Hesse, Germany (GRID:grid.507846.8) 
245 1 |a Navigating AI conformity: A design framework to assess fairness, explainability, and performance 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Interviews 
653 |a Artifacts 
653 |a Assessments 
653 |a Organizations 
653 |a Knowledge management 
653 |a Fairness 
653 |a Ambiguity 
653 |a Conformity 
653 |a Owners 
653 |a Artificial intelligence 
653 |a Design analysis 
653 |a Research design 
653 |a Regulation 
653 |a Frame analysis 
653 |a Knowledge based engineering 
653 |a Writers 
653 |a Evaluation 
700 1 |a Zacharias, Jan  |u Goethe University, Information Systems and Information Management, Hesse, Germany (GRID:grid.507846.8) 
700 1 |a Lowin, Maximilian  |u Goethe University, Information Systems and Information Management, Hesse, Germany (GRID:grid.507846.8) 
700 1 |a Chen, Johannes  |u Goethe University, Information Systems and Information Management, Hesse, Germany (GRID:grid.507846.8) 
700 1 |a Hinz, Oliver  |u Goethe University, Information Systems and Information Management, Hesse, Germany (GRID:grid.507846.8) 
773 0 |t Electronic Markets  |g vol. 35, no. 1 (Dec 2025), p. 24 
786 0 |d ProQuest  |t ABI/INFORM Global 
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