Ensuring transparency in AI agents primarily involves implementing Explainable AI (XAI) techniques, which allow for human-understandable insights into their decision-making processes. This includes developing models that can provide interpretable explanations for their outputs, rather than operating as opaque "black boxes." Furthermore, comprehensive logging and audit trails are crucial, documenting every action, input, and output an AI agent processes, creating a verifiable history. Robust documentation covering model architecture, training data sources, and intended use cases is equally vital for external scrutiny. Adopting open-source principles for model code and data, where feasible, facilitates community review and identification of biases or flaws. Regular, independent third-party audits can also validate an AI's operational integrity and adherence to ethical guidelines. Ultimately, a combination of these approaches fosters trust and accountability by making AI decisions comprehensible and auditable. More details: https://uadvokata.com.ua/