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Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards

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  • Cosmin Cernăzanu-Glăvan

    (Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, Romania
    These authors contributed equally to this work.)

  • Andrei-Ștefan Bulzan

    (Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, Romania
    These authors contributed equally to this work.)

Abstract

Despite the ideal of a unified Single Market, a powerful “home bias” pervades EU public procurement, hinting at unseen barriers that conventional analysis fails to capture. This study introduces an interpretable AI framework to investigate these dynamics, pairing a LightGBM model with SHapley Additive exPlanations (SHAP) to examine the vast Tenders Electronic Daily (TED) database (2018–2023). Concretely, we propose a fuzzy linguistic layer that translates SHAP’s complex quantitative outputs into intuitive, human-readable terms. Our model effectively distinguishes local from non-local awards (AUC ≈ 0.855), revealing that while high-value contracts expectedly attract broader competition, the most potent predictors are a country’s own history of local awards and structural factors like the buyer’s type and location. This points not to isolated incidents, but, rather, to deep-seated patterns shaping market fairness. Our combined XAI-Fuzzy approach offers a new instrument for transparent governance, enabling policymakers to diagnose market realities and forge a more genuinely open and equitable European public square.

Suggested Citation

  • Cosmin Cernăzanu-Glăvan & Andrei-Ștefan Bulzan, 2025. "Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards," Mathematics, MDPI, vol. 13(13), pages 1-21, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2215-:d:1696548
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