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Designing a feature selection method based on explainable artificial intelligence

Author

Listed:
  • Jan Zacharias

    (Goethe University Frankfurt)

  • Moritz Zahn

    (Goethe University Frankfurt)

  • Johannes Chen

    (Goethe University Frankfurt)

  • Oliver Hinz

    (Goethe University Frankfurt)

Abstract

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.

Suggested Citation

  • Jan Zacharias & Moritz Zahn & Johannes Chen & Oliver Hinz, 2022. "Designing a feature selection method based on explainable artificial intelligence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2159-2184, December.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:4:d:10.1007_s12525-022-00608-1
    DOI: 10.1007/s12525-022-00608-1
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    References listed on IDEAS

    as
    1. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    2. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    3. Kai Jia & Nan Zhang, 2022. "Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 59-71, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Explainable artificial intelligence; Machine learning; Feature selection; Design science research; SHAP values; Preprocessing;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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