Cost-Aware Counterfactuals for Black Box Explanations
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
A comprehensive benchmark is crucial for evaluating automated Business Intelligence (BI) systems and their real-world effectiveness. We propose BI-Bench, a holistic, end-to-end benchmarking framework that assesses BI systems based on the quality, relevance, and depth of insights. It categorizes queries into descriptive, diagnostic, predictive, and prescriptive types, aligning with practical BI needs. Our fully automated approach enables custom benchmark generation tailored to specific datasets. Additionally, we introduce an automated evaluation mechanism within BI-Bench that removes reliance on strict ground truth, ensuring scalable and adaptable assessments. By addressing key limitations, it offers a flexible and robust, user-centered methodology for advancing next-generation BI systems.
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Lina Berrayana, Sean Rooney, et al.
ACL 2025
Navve Wasserman, Roi Pony, et al.
ACL 2025
Svetoslav Nizhnichenkov, Rahul Nair, et al.
ECML PKDD 2025