Hibrit Açıklanabilir Yapay Zeka Tasarımı ve LIME Uygulaması

Year 2021, Issue: 27, 228 - 236, 30.11.2021

Ayça Çakmak Pehlivanlı Rahmi Ahmet Selim Deliloğlu

https://doi.org/10.31590/ejosat.959030

Cited By: 3

Abstract

Günümüz teknolojisinin hızlı gelişimi ile yapay zeka günlük yaşantının bir çok alanında vazgeçilmez hale gelmiştir. Özellikle yanlış karar verme maliyetinin yüksek olduğu finans, sağlık, hukuk gibi alanlarda kullanılmaya başlamasına rağmen bu alanlardaki düzenlemeler nedeni ile kısıtlı düzeyde kullanılabilmektedir. Bu kısıtlamanın en temel nedeni de elde edilen yüksek performanslı sonuçların açıklanabilirliklerinin düşük olmasıdır. Bu çalışma kapsamında açıklanabilirliği yüksek hibrit bir tasarım önerilmiş, finans ve sağlık alanlarından elde edilmiş farklı veri setlerine uygulanmıştır. Şeffaf, hibrit ve açıklanabilirlik olmak üzere üç temel aşamada gerçekleştirilen hibrit yaklaşımın açıklanabilirlik aşamasında yerel olarak seçilen gözlemlerin tahmininde değişkenlerin etkisini belirlemek için LIME ölçütü kullanılmış ve sonuçlar yorumlanmıştır.

Keywords

Açıklanabilir Yapay Zeka, LIME, Karar Ağaçları, Rasgele Orman

Hybrid Explainable Artifical Intelligence Design and LIME Application

Abstract

With the rapid development of today's technology, artificial intelligence has become indispensable in many areas of daily life. Although it has started to be used in the areas such as finance, health and law, where the cost of making wrong decisions is high, it can be used at a limited level due to the regulations in these areas. The main reason for this limitation is the low explainability of the high-performance results obtained. Within the scope of this study, a hybrid design with high explainability was proposed and applied to different datasets obtained from the fields of finance and health. The LIME criterion was used to determine the effect of variables on the estimation of locally selected observations in the explainability phase of the hybrid approach, which was carried out in three basic stages as transparent, hybrid and explainability, and the results were interpreted.

Keywords

Explanaible Artificial Intelligence, LIME, Decision Trees, Random Forest

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