The term interactive explainability is particularly important in the areas of artificial intelligence, big data, smart data and digital transformation. It describes the ability of modern technologies, especially AI systems, not only to explain their decisions and procedures, but also to actively involve users. This makes it possible for users to ask specific questions and better understand the background to automated recommendations or predictions.
Imagine a company using AI to pre-select applicants. Thanks to interactive explainability, the HR department can ask directly why a particular candidate was put forward. The AI provides an answer and shows, for example, that professional experience or certain skills were the deciding factor. Employees can respond to this and ask further questions - for example, how important individual criteria were or which applicants had similar ratings.
Interactive explainability creates more trust in data-based systems. It helps decision-makers to understand automated processes and adapt them if necessary. This reduces risks and improves collaboration between man and machine - a decisive advantage in an increasingly digitalised working world.
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