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

Cosmin Cernăzanu-Glăvan and Andrei-Ștefan Bulzan ()
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Cosmin Cernăzanu-Glăvan: Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, Romania
Andrei-Ștefan Bulzan: Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, Romania

Mathematics, 2025, vol. 13, issue 13, 1-21

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.

Keywords: public procurement; Explainable AI (XAI); SHAP; fuzzy linguistic interpretation; smart governance; Tenders Electronic Daily (TED) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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