Problems of Interpretability and Transparency of Decisions Made by AI

Abstract

The relevance of the problem of interpretability and transparency of decisions in artificial intelligence (AI) systems is determined by both the expansion of their application in critical areas and the need to comply with ethical and legal standards. The introduction of methods for interpreting and ensuring transparency in the operation of AI systems is an important step towards increasing user confidence, improving the quality of decisions made, and minimizing the risks associated with the use of AI in real life. The issues of interpretability and transparency of artificial intelligence require not only the attention of developers and researchers, but also the creation of appropriate standards aimed at improving the explainability of AI solutions and their accessibility to users. It is important to note that solving this problem is becoming critical to ensuring the safety and reliability of AI systems, as well as to minimizing the potential risks of their use in everyday life. Interpretability and transparency of AI systems are crucial for their widespread use, as they enable verification, increase trust, and reduce the risk of errors in decision making. In this regard, research and development in the field of interpretable AI models continues to be one of the most important tasks in the modern scientific and technological community.