trustworthiness

The purpose of this study was to develop a scale for measuring the service quality in sport scientific journals. A mixed approach was conducted to fulfill the research objectives. In qualitative phase 15 sport paper writer were interviewed and in quantitative phase, 357 sport researchers were studied through systematic random sampling. The face and content validity of the scale was confirmed by 15 experts and the final questionnaire of the scientific journals service quality was provided to 29 subjects. 26 items were ranked in five factors (accountability speed, executive structure, trustworthiness, employees and updating) based on exploratory factor analysis with orthogonal rotation. Cronbach's alpha, KMO, Bartlett Test and confirmatory factor analysis were used by SPSS and LISREL for data analysis. It is worth noting that the results of confirmatory factor analysis and Cronbach's alpha coefficient (0.91) supported the five-factor structure of JourQual scale and confirmed its validity and reliability.

Objective: This review explores the trustworthiness of multimodal artificial intelligence (AI) systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of key tasks such as Visual Question Answering (VQA), image captioning, and visual dialogue. Background: Multimodal models, particularly vision-language models, enhance artificial intelligence (AI) capabilities by integrating visual and textual data, mimicking human learning processes. Despite significant advancements, the trustworthiness of these models remains a crucial concern, particularly as AI systems increasingly confront issues regarding fairness, transparency, and ethics. Methods: This review examines research conducted from 2017 to 2024, focusing on forenamed core vision-language tasks. It employs a comparative approach to analyze these tasks through the lens of trustworthiness, underlining fairness, explainability, and ethics. This study synthesizes findings from recent literature to identify trends, challenges, and state-of-the-art solutions. Results: Several key findings were highlighted. Transparency: The explainability of vision language tasks is important for user trust. Techniques, such as attention maps and gradient-based methods, have successfully addressed this issue. Fairness: Bias mitigation in VQA and visual dialogue systems is essential for ensuring unbiased outcomes across diverse demographic groups. Ethical Implications: Addressing biases in multilingual models and ensuring ethical data handling is critical for the responsible deployment of vision-language systems. Conclusion: This study underscores the importance of integrating fairness, transparency, and ethical considerations in developing vision-language models within a unified framework.