Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards
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 Additi...
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/13/2215 |
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| author | Cosmin Cernăzanu-Glăvan Andrei-Ștefan Bulzan |
| author_facet | Cosmin Cernăzanu-Glăvan Andrei-Ștefan Bulzan |
| author_sort | Cosmin Cernăzanu-Glăvan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-90148fb76cde4a3e856d8a42b1bc1dd8 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-90148fb76cde4a3e856d8a42b1bc1dd82025-08-20T02:36:32ZengMDPI AGMathematics2227-73902025-07-011313221510.3390/math13132215Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender AwardsCosmin Cernăzanu-Glăvan0Andrei-Ștefan Bulzan1Department of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, RomaniaDepartment of Computer and Information Technology, Politehnica University Timișoara, 300223 Timișoara, RomaniaDespite 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.https://www.mdpi.com/2227-7390/13/13/2215public procurementExplainable AI (XAI)SHAPfuzzy linguistic interpretationsmart governanceTenders Electronic Daily (TED) |
| spellingShingle | Cosmin Cernăzanu-Glăvan Andrei-Ștefan Bulzan Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards Mathematics public procurement Explainable AI (XAI) SHAP fuzzy linguistic interpretation smart governance Tenders Electronic Daily (TED) |
| title | Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards |
| title_full | Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards |
| title_fullStr | Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards |
| title_full_unstemmed | Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards |
| title_short | Explainable AI and Fuzzy Linguistic Interpretation for Enhanced Transparency in Public Procurement: Analyzing EU Tender Awards |
| title_sort | explainable ai and fuzzy linguistic interpretation for enhanced transparency in public procurement analyzing eu tender awards |
| topic | public procurement Explainable AI (XAI) SHAP fuzzy linguistic interpretation smart governance Tenders Electronic Daily (TED) |
| url | https://www.mdpi.com/2227-7390/13/13/2215 |
| work_keys_str_mv | AT cosmincernazanuglavan explainableaiandfuzzylinguisticinterpretationforenhancedtransparencyinpublicprocurementanalyzingeutenderawards AT andreistefanbulzan explainableaiandfuzzylinguisticinterpretationforenhancedtransparencyinpublicprocurementanalyzingeutenderawards |