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All Journal International Transactions on Artificial Intelligence (ITALIC) BEAM
Rifqa Nabila Muti
CAI Sejahtera Indonesia, Indonesia
Computer Science & IT Control & Systems Engineering Library & Information Science

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Trustworthy Machine Learning Evaluation Framework for Robust and Interpretable Intelligent Systems Ninda Lutfiani; Sutarto Wijono; Rifqa Nabila Muti; Yasir Mustafa Kareem
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1067

Abstract

Artificial intelligence (AI) deployment in critical domains requires machine learning systems that are not only accurate but also robust, interpretable, fair, and aligned with responsible governance principles. However, conventional machine learning evaluation approaches often prioritize predictive performance and computational efficiency while giving limited attention to ethical accountability, transparency, regulatory compliance, and sustainability. This study aims to develop a trustworthy machine learning evaluation framework for robust and interpretable AI systems. The focus of the study is the evaluation of intelligent systems across healthcare, finance, and transportation, where reliability and accountability are essential for real-world deployment. A qualitative case study approach was employed through expert interviews, literature analysis, document review, and cross-domain case comparisons to identify key evaluation dimensions. The findings show that trustworthy evaluation should integrate technical indicators, including accuracy, robustness, and interpretability, with broader dimensions such as fairness, accountability, governance compliance, and social responsibility. The proposed framework provides a structured model for assessing intelligent systems beyond conventional performance metrics. It also supports better consistency in interpretability assessment, stronger fairness evaluation, and improved alignment with international AI governance expectations. This study contributes to the development of responsible AI by offering a practi- cal evaluation framework that can guide researchers, developers, and institutions in designing machine learning systems that are reliable, transparent, and socially accountable. The framework has implications for sustainable and compliant AI implementation in high-impact sectors.
Artificial Intelligence Driven Audience Sentiment Analytics for Interactive Digital Broadcasting Platforms Richard Andre Sunarjo; Tessa Handra; Rifqa Nabila Muti; Kamal Arif Al-Farouqi
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid growth of interactive digital broadcasting platforms has significantly transformed the way audiences engage with media content through live chats, comments, and social media interactions. However, the massive volume of usergenerated feedback creates challenges for broadcasters in understanding audience sentiment efficiently. This study aims to analyze audience sentiment using Artificial Intelligence (AI) driven analytics to improve the understanding of audience engagement in interactive digital broadcasting platforms. The research applies a quantitative approach using AI based Natural Language Processing (NLP) techniques to process and analyze audience feedback data collected from comments, live chat interactions, and social media responses related to digital broadcast content. The analytical process includes data preprocessing, sentiment classification, and machine learning based modeling to identify patterns of audience emotional responses and engagement. The findings indicate that AI driven sentiment analytics can effectively classify audience opinions and detect real time sentiment trends associated with broadcasted content. The results also demonstrate that AI-based analysis enables broadcasters to gain deeper insights into audience preferences, evaluate content performance, and optimize broadcasting strategies more efficiently compared with conventional manual analysis methods. In conclusion, the integration of AI in audience sentiment analytics offers a valuable approach for enhancing audience understanding and supporting data-driven decision-making in modern digital broadcasting ecosystems while promoting more responsive and personalized media experiences.
Co-Authors Kamal Arif Al-Farouqi Ninda Lutfiani Richard Andre Sunarjo Sutarto Wijono Tessa Handra Yasir Mustafa Kareem