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Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, Tadej Završnik, 2023, original scientific article

Abstract: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Keywords: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Published in DKUM: 14.03.2024; Views: 212; Downloads: 19
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Comparative analysis of collaborative and simulation based learning in the management environment
Mirjana Kljajić Borštnar, 2012, original scientific article

Abstract: Purpose of the study is to compare two different approaches to the collaborative problem solving one in a highly controlled laboratory experiment: Optimisation of business politics using business simulator at different experimental condition which reflect different feedback information structure and one in a collaborative environment of the social media, characterised by non-structured, rule-free and even chaotic feedback information. Comparative analyses of participant’s opinion who participate in experiments have been considered in order to find common characteristics relevant for group/collaborative problem solving. Based on these findings a general explanatory causal loop model of collaborative learning during problem solving was built.
Keywords: group decision support, information structure, collaborative learning, simulation model
Published in DKUM: 10.07.2015; Views: 1485; Downloads: 376
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