Unveiling the Potential of Counterfactuals Explanations in Employability
This work addresses the need for applied XAI in real-world employability contexts, though it is incremental as it extends existing methods to a new domain.
The study applied counterfactual explanations to employability problems using real data from a Belgian employment institution, demonstrating their utility beyond mere explanations for enhancing decision support, legal compliance, and insight analysis.
In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions. However, we are yet to see more applied studies in which they are applied in real-world cases. To fill this gap, this study focuses on showing how counterfactuals are applied to employability-related problems which involve complex machine learning algorithms. For these use cases, we use real data obtained from a public Belgian employment institution (VDAB). The use cases presented go beyond the mere application of counterfactuals as explanations, showing how they can enhance decision support, comply with legal requirements, guide controlled changes, and analyze novel insights.