Transparentnost umělé inteligence: Kritéria, technická řešení a jejich dopad na odpovědnost uživatele

This thesis focuses on the transparency and explainability of artificial intelligence (AI) systems, distinguishing between the two concepts and emphasizing their mutual necessity. Transparency is understood as the availability of information about a system's functioning, while explainability refers to the user's ability to comprehend and meaningfully use this information. The aim of this work is to propose a framework for transparency criteria that inherently supports the explainability of AI outputs. The thesis argues that only the combined fulfilment of both transparency and explainability can ensure responsible deployment, foster trust, and enable the widespread use of AI in professional, scientific, and private contexts. The research integrates literature review, case study analysis, and the examination of documentation frameworks, all guided by a design-oriented methodology. The result is a conceptual proposal for a multi-level transparency evaluation framework that advances a user-centered and practically applicable understanding of transparency in AI.