@article{article_1924721, title={A Hybrid Framework for Identifying and Weighting Artificial Intelligence Bias Criteria in Organizational Information Systems}, journal={Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji}, volume={14}, pages={903–913}, year={2026}, DOI={10.29109/gujsc.1924721}, url={https://izlik.org/JA85GN88NX}, author={Asiloğulları Ayan, Merve}, keywords={Yapay zeka önyargısı, Kurumsal bilgi sistemleri, ERP, Bulanık AHP, Algoritmik yönetişim}, 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.}, number={2}