TY  - JOUR
T1  - Yapay Zekâ Tabanlı Karar Destek Sistemlerinde Teknik, Etik ve Yönetişimsel Yaklaşımlar: Çok Sektörlü Bir Derleme Çalışması
TT  - Technical, Ethical, And Governance Approaches in Artificial Intelligence-Based Decision Support Systems: A Multi-Sectoral Review
AU  - Kesgin, Kadir
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.33461/uybisbbd.1691218
JF  - International Journal of Management Information Systems and Computer Science
JO  - UYBİSBBD
PB  - Adem KORKMAZ
WT  - DergiPark
SN  - 2618-5954
SP  - 29
EP  - 48
VL  - 9
IS  - 1
LA  - tr
AB  - Bu derleme çalışması, yapay zekâ tabanlı karar destek sistemlerinin (YZ-KDS) teknik, etik ve yönetişim boyutlarını sistematik bir analiz çerçevesinde incelemeyi amaçlamaktadır. Sağlık, finans, üretim/lojistik ve çevre yönetimi gibi kritik sektörlerde YZ-KDS sistemlerinin uygulamaları, veri kalitesi, açıklanabilirlik, adalet ve hesap verebilirlik bağlamında ele alınmıştır. Çalışmanın veri temeli, Kesgin ve Dözer (2025) tarafından geliştirilen BiBLoX yazılımı aracılığıyla Web of Science, Scopus ve TR Dizin veri tabanlarından çekilen 4824 bibliyografik kayda dayanmaktadır. PRISMA protokolü doğrultusunda yapılan filtreleme sonucunda 1273 yayın detaylı incelemeye alınmıştır. Bibliyometrik analiz sonuçlarına göre, açıklanabilir yapay zekâ çerçeveleri (XAI), federated learning tabanlı gizlilik çözümleri ve adil karar mekanizmaları, sistemlerin güvenilirliğini artırmada öne çıkan yaklaşımlar olarak belirlenmiştir. Ayrıca disiplinlerarası iş birliği, etik denetim ve kurumsal yönetişim süreçlerinin, bu sistemlerin sorumlu ve sürdürülebilir biçimde geliştirilmesi açısından kritik olduğu vurgulanmaktadır. Çalışma, yapay zekâ destekli karar destek sistemlerinin gelecekteki tasarımlarına hem teorik hem uygulamalı katkı sunmayı hedeflemektedir.
KW  - yapay zekâ
KW  - karar destek sistemleri
KW  - açıklanabilirlik
KW  - federated learning
KW  - veri yönetişimi
KW  - etik yapay zekâ
N2  - This review study aims to systematically examine the technical, ethical, and governance dimensions of artificial intelligence-based decision support systems (AI-DSS). The applications of AI-DSS across critical sectors such as healthcare, finance, manufacturing/logistics, and environmental management are analyzed in terms of data quality, explainability, fairness, and accountability. The bibliometric foundation of the study is based on 4,824 bibliographic records retrieved from Web of Science, Scopus, and TR Dizin databases using the open-source BiBLoX software developed by Kesgin and Dözer (2025). Following the PRISMA protocol, a multi-stage screening process was implemented, resulting in a refined corpus of 1,273 publications subjected to detailed analysis. The findings highlight explainable AI (XAI) frameworks, federated learning-based privacy solutions, and fairness-aware decision mechanisms as key approaches to enhancing system reliability. Moreover, interdisciplinary collaboration, ethical oversight, and institutional governance structures are emphasized as critical enablers for the responsible and sustainable development of AI-DSS. This study aims to contribute a conceptual and practical perspective to the design of future decision support systems grounded in artificial intelligence.
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UR  - https://doi.org/10.33461/uybisbbd.1691218
L1  - https://dergipark.org.tr/tr/download/article-file/4835976
ER  -