Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations

ArXi:2604.09573v1 Announce Type: cross Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study was to investigate and evaluate how different types of counterfactual explanations, namely single point explanations and interval-based explanations, affect both model understanding and (nstrated) trust.