Designing a feature selection method based on explainable artificial intelligence
Open accessAbstract
Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes domains, including credit-risk assessment and medical diagnostics. Consequently, AI systems increasingly affect humans, yet many state-of-the-art systems lack transparency and thus, deny the individual’s “right to explanation”. As a remedy, researchers and practitioners have developed explainable AI, which provides reasoning on how AI systems infer individual predictions. However, with recent legal initiatives demanding comprehensive explainability throughout the (development of an) AI system, we argue that the pre-processing stage has been unjustifiably neglected and should receive greater attention in current efforts to establish explainability. In this paper, we focus on introducing explainability to an integral part of the pre-processing stage: feature selection. Specifically, we build upon design science research to develop a design framework for explainable feature selection. We instantiate the design framework in a running software artifact and evaluate it in two focus group sessions. Our artifact helps organizations to persuasively justify feature selection to stakeholders and, thus, comply with upcoming AI legislation. We further provide researchers and practitioners with a design framework consisting of meta-requirements and design principles for explainable feature selection.
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Feature importance technique seeks to improve explanatory depth by pinpointing the most significant features influencing an AI model's decisions.
“Zacharias et al. [169] explained that the feature importance technique seeks to improve explanatory depth by pinpointing the most significant features influencing an AI model's decisions.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [169]
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- Authors
- Zacharias, J., von Zahn, M., Chen, J., & Hinz, O.
- Affiliations
- Goethe University Frankfurt (DE)
- Corresponding author
- Zacharias, Jan
- Venue
- Electronic Markets
- Published
- 2022-12
- Volume
- 32
- Issue
- 4
- Pages
- 2159-2184
- ISSN
- 1019-6781 (ISSN-L)
- 1422-8890
- OpenAlex
- W4312132544
- Cited by
- 109
- References
- 91
- Citations by year
- 2026: 28 · 2025: 31 · 2024: 31 · 2023: 18 · 2022: 1
- FWCI
- 6.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Electronic Markets (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Funders
- Johann Wolfgang Goethe-Universität, Frankfurt am Main
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- explainable AI, feature selection, right to explanation, AI transparency, design framework, meta-requirements, design principles, credit risk assessment, medical diagnostics
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