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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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“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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Metadata
- 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)
- Physical Sciences › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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