Explainable Machine Learning untuk Mendukung Pengambilan Keputusan yang Adil dan Transparan

Wiki Lofandri

Abstract


The use of machine learning to support decision-making is increasingly widespread in various fields. However, many machine learning models are highly complex, making their decision-making processes difficult to understand. This situation has the potential to raise issues related to transparency, accountability, and fairness in data-driven decisions. Therefore, an approach is needed that can explain how the model works without compromising its predictive value.
This research aims to examine the application of explainable machine learning as an approach to supporting fair and transparent decision-making. The research method involves building a machine learning model using commonly used algorithms and then applying explainability techniques to analyze the contribution of each feature to the prediction results. Model evaluation focuses not only on predictive performance but also on the model's interpretability in explaining the resulting decisions.
The analysis shows that the application of explainable machine learning can provide a better understanding of the model's decision-making process and help identify significantly influential factors. This approach enables users to evaluate model decisions more rationally and responsibly. Thus, explainable machine learning has the potential to become a critical component in the development of machine learning-based decision support systems that emphasize fairness and transparency.

Keywords


Explainable Machine Learning; Interpretability; Transparency; Fairness; Decision Making

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References


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DOI: https://doi.org/10.15548/isrj.v6i02.13746

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Gedung Fakultas Sains dan Teknologi 
Kampus III Universitas Islam Negeri Imam Bonjol Padang
Sungai Bangek, Kec. Koto Tangah, Kota Padang, Sumatera Barat

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