Aug 2026· Formosa Journal of Science and Technology· Vol 5, pp. 2213-2228· 0 citations
TL;DR
The results show that clarity of decision reasons, consistency of explanations, traceability of processes, and suitability of context are the main elements of forming trust.
Abstract
The application of artificial intelligence in decision support systems faces transparency challenges that can reduce user confidence in the results of recommendations. This research aims to develop an Explainable Artificial Intelligence framework that supports a transparent, understandable, and accountable decision support system. The research used an exploratory qualitative approach with semi-structured interviews with 20 informants consisting of AI developers, information systems experts, and professional users. The data was analyzed using thematic analysis to identify the need for explanation, confidence-building factors, and the characteristics of information that were considered relevant. The results show that clarity of decision reasons, consistency of explanations, traceability of processes, and suitability of context are the main elements of forming trust. This research produces an XAI conceptual framework that can be the basis for the development of a more transparent, reliable, and user-oriented decision support system
[1]. Arh, T., & Blaži, B. J. (2007). Application of Multi-Attribute Decision Making Approach to Learning Management Systems Evaluation . JOURNAL OF COMPUTERS, 2(10), 28-37. [2]. BÂRA, A., BOTHA, I., LUNGU, I., & OPREA, S. V. (2013). Decision Support System in National Power Companies A Practical Example (Part I) . Data...
H. Singh· International Journal of Sci...· 0 citations
The effectiveness of modern high-performance AI applications in a number of critical areas – medicine, security, transportation, etc. – is limited by the inability to explain the results of their work. The lack of explanation in decision-making undermines users' trust in digital products and services. One of the fruitf...
M. Mikheyenkova· Artificial Societies· 0 citations
Purpose – This study validates a multi-criteria decision-support framework for assessing organisational readiness for Artificial Intelligence (AI) adoption in public administration, with a specific focus on municipal administration. Validation ensures that the results are conceptually sound, credible, reliable, and rob...
Primož Pevcin, Katja Debelak, Rok Hržica· Business, Management and Eco...· 1 citation
The study suggests that explainable AI is a crucial factor in building intelligent, reliable, accountable, transparent, and effective AI-powered systems that can be deployed in realistic environments for decision making.
Kirankumar Pundlik Mohurle, S. Sahare, Yugant Rupesh Dhoke et al.· International Journal of Eng...· 0 citations
The growing adoption of artificial intelligence (AI) in organizational and tactical decision-making has intensified concerns about transparency, accountability and the changing role of human decision-making in high-risk decision environments. Explainable artificial intelligence (XAI) has developed as a prominent approa...
Anthonio Martono, Danang Prihandoko· International Conferences on...· 0 citations
Artificial intelligence increasingly informs consequential enterprise decisions, yet prevailing governance approaches tend to assess ethical principles, model properties, regulatory conformity or audit activities in isolation. This conceptual paper argues that trustworthy AI governance should focus on the organizationa...
Jyothish Kumar Nair , Ajaykumar Nediyazth Ayyappan Nair· Journal of Business Practice...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
