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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 14 Sayı: 2

A Hybrid Framework for Identifying and Weighting Artificial Intelligence Bias Criteria in Organizational Information Systems

Kurumsal Bilgi Sistemlerinde Yapay Zeka Önyargı Kriterlerini Belirleme ve Ağırlıklandırma için Hibrit Bir Çerçeve

Merve Asiloğulları Ayan(MİLLİ SAVUNMA ÜNİVERSİTESİ, ALPARSLAN SAVUNMA BİLİMLERİ ENSTİTÜSÜ, SAVUNMA YÖNETİMİ ANABİLİM DALI)0000-0001-7522-0911
Pages
903–913
Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal three main bias factors: Machine Learning (w = 0.5304), Fairness and Ethics (w = 0.3043), and Natural Language Processing (w = 0.1653).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Integrating AI into OIS poses several challenges in terms of governance due to the possibility of algorithmic bias.
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Key Findings & Data:Even though there is increased consciousness about AI bias, no systematic criteria have been put forward for prioritizing bias factors in OIS.
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Conclusion & Impact:The findings reveal three main bias factors: Machine Learning (w = 0.5304), Fairness and Ethics (w = 0.3043), and Natural Language Processing (w = 0.1653).
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zeka önyargısı · Kurumsal bilgi sistemleri · ERP

Abstract

Integrating AI into OIS poses several challenges in terms of governance due to the possibility of algorithmic bias. In ERP systems, AI-driven modules play a critical role in making key decisions related to employee evaluation, resource management, demand prediction, and procurement of suppliers. With biased data, erroneous modeling assumptions, and black-box decision-making, the impact could be adverse to particular user categories, skewing organizational processes and potentially harming the company's reputation and regulatory compliance. Even though there is increased consciousness about AI bias, no systematic criteria have been put forward for prioritizing bias factors in OIS. This paper proposes a novel hybrid approach to identifying and prioritizing AI bias criteria in OIS through a combination of literature review and Fuzzy-AHP. The expert opinions of five AI and OIS specialists were used to generate criterion weights under fuzziness conditions. The findings reveal three main bias factors: Machine Learning (w = 0.5304), Fairness and Ethics (w = 0.3043), and Natural Language Processing (w = 0.1653). From the list of sub-criteria, Training Data Quality (GW = 0.2430), Model Explainability (GW = 0.1345), and Accountability and Transparency (GW = 0.1304) are the most significant factors. The suggested methodology provides a framework for aiding OIS design, IT management, and organizational policy formulation on AI bias governance.

Özet

Yapay zekanın (YZ) kurumsal bilgi sistemlerine (KBS) entegrasyonu, algoritmik önyargı olasılığı nedeniyle yönetişim açısından çeşitli zorluklar ortaya koymaktadır. Kurumsal kaynak planlama (ERP) sistemlerinde, yapay zeka destekli modüller, çalışan değerlendirmesi, kaynak yönetimi, talep tahmini ve tedarikçi alımı ile ilgili önemli kararların alınmasında kritik bir rol oynamaktadır. Önyargılı veriler, hatalı modelleme varsayımları ve kara kutu karar verme ile, etki belirli kullanıcı kategorileri için olumsuz olabilir, organizasyonel süreçleri çarpıtabilir ve potansiyel olarak şirketin itibarını ve mevzuat uyumluluğunu zedeleyebilir. Yapay zeka önyargısı konusunda artan bir farkındalık olmasına rağmen, KBS’de önyargı faktörlerini önceliklendirmek için sistematik bir kriter ortaya konmamıştır. Bu makale, literatür taraması ve Bulanık AHP’nin bir kombinasyonu yoluyla KBS’de yapay zeka önyargı kriterlerini belirlemek ve önceliklendirmek için yeni bir hibrit yaklaşım önermektedir. Bulanıklık koşulları altında kriter ağırlıklarını oluşturmak için beş yapay zeka ve KBS uzmanının uzman görüşleri kullanılmıştır. Bulgular üç ana önyargı faktörünü ortaya koymaktadır: Makine Öğrenimi (w = 0,5304), Adalet ve Etik (w = 0,3043) ve Doğal Dil İşleme (w = 0,1653). Alt kriterler listesinden, Eğitim Veri Kalitesi (GW = 0,2430), Model Açıklanabilirliği (GW = 0,1345) ve Hesap Verebilirlik ve Şeffaflık (GW = 0,1304) en önemli faktörlerdir. Önerilen metodoloji, KBS tasarımına, BT yönetimine ve yapay zeka önyargı yönetimi konusunda kurumsal politika oluşturulmasına yardımcı olacak bir çerçeve sağlamaktadır.

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Cite This Article

Ayan, M. A. (2026). A Hybrid Framework for Identifying and Weighting Artificial Intelligence Bias Criteria in Organizational Information Systems. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 14 Sayı: 2, 903–913. https://doi.org/10.29109/gujsc.1924721

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