하태현 교수

AbstractWith advancements in artificial intelligence (AI), explainable AI (XAI) has emerged as a promising tool for enhancing the explainability of complex machine learning models. However, the explanations generated by an XAI may lead to cognitive biases among human users. To address this problem, this study aims to investigate how to mitigate users’ cognitive biases based on their individual characteristics. In the literature review, we found two factors that can be helpful in remedying biases

Contrary to the traditional concept of artificial intelligence, explainable artificial intelligence (XAI) aims to provide explanations for the prediction results and make users perceive the system as being reliable. However, despite its importance, only a few studies have investigated how the explanations of an XAI system should be designed. This study investigates how people attribute the perceived ability of XAI systems based on perceived attributional qualities and how the power status of the

Purpose The purpose of this paper is to investigate how we can understand social media interactions better by explicating the process of social capital formation on Facebook from a reciprocity perspective. Design/methodology/approach This study observed users who got tagged on Facebook by his/her friends and how s/he responded to that tagging activity. In total, 4,666 posts and 418,580 comments from The New York Times Facebook page were collected for the observation. Findings A majority (77.87 p

Abstract DC (Direct current) is used in electrokinetic (EK) technology to extract hazardous materials from soils. Besides, AC (alternating current) electric field is also used to induce particle and fluid motion in electrokinetics. The influence of AC and DC on electrokinetic phenomena was studied for the removal of nitrate complex in soil environment. The experiments were performed by employing three systems – DC, AC, and AC overlapped DC. The removal of cations was higher at the anodic spot wh

Explainability and accuracy of the machine learning algorithms usually laid on a trade-off relationship. Several algorithms such as deep-learning artificial neural networks have high accuracy but low explainability. Since there were only limited ways to access the learning and prediction processes in algorithms, researchers and users were not able to understand how the results were given to them. However, a recent project, explainable artificial intelligence (XAI) by DARPA, showed that AI system