The term Interactive Explainability is particularly important in the fields of Artificial Intelligence, Big Data and Smart Data, as well as 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 to better understand the background of automated recommendations or predictions.
Imagine a company using AI to shortlist candidates. Thanks to interactive explainability, the HR department can directly query why a particular candidate was suggested. The AI responds, indicating, for instance, that work experience or specific skills were decisive. Employees can react to this and ask further questions – for example, how important individual criteria were or which candidates had similar ratings.
Interactive explainability fosters greater trust in data-based systems. It helps decision-makers understand automated processes and adapt them if necessary. This reduces risks and improves human-machine collaboration – a crucial advantage in an increasingly digital working world.
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