The complexity and opacity of AI algorithms can make it difficult to understand how they arrive at decisions. This lack of transparency raises important questions about accountability, especially in high-stakes areas like healthcare, law, or resource allocation. Transparency is essential in academic research applied to all these and other fields Konwar, 2025. When AI systems are used to suggest or to make decisions about treatments or resource distribution, unclear processes can lead to mistrust and harmful outcomes.
Transparency is therefore not just a technical consideration; it’s a core ethical requirement for trustworthy AI. A recent study highlights this, emphasizing that transparency should be embedded not only in the final outputs but throughout the entire AI development and deployment lifecycle Sebastiao, 2025. It also points out significant differences in how transparency is understood and applied across various application fields, depending on cultural, political, and economic contexts. While transparency is widely encouraged, the study stresses a persistent gap: the lack of concrete, practical guidance for implementation in real-world systems.
At scienceOS, we are committed to addressing this gap. We provide information about how scienceOS works, display the internal processes of the AI agent, and provide templates to extensively and effortlessly disclose the use of scienceOS.
