Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence
<|reference_start|>Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence: Explainable Artificial Intelligence (XAI) is a pivotal research domain aimed at understanding the operational mechanisms of AI systems, particularly those considered ``black boxes'' due to their complex, opaque nature. XAI seeks to make these AI systems more understandable and trustworthy, providing insight into their decision-making processes. By producing clear and comprehensible explanations, XAI enables users, practitioners, and stakeholders to trust a model's decisions. This work analyses the value of data morphology strategies in generating counterfactual explanations. It introduces the Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF) method, a model-agnostic counterfactual generator that leverages data morphology to estimate a model's decision boundaries. The ONB-MACF method constructs hyperspheres in the data space whose covered points share a class, mapping the decision boundary. Counterfactuals are then generated by incrementally adjusting an instance's attributes towards the nearest alternate-class hypersphere, crossing the decision boundary with minimal modifications. By design, the ONB-MACF method generates feasible and sparse counterfactuals that follow the data distribution. Our comprehensive benchmark from a double perspective (quantitative and qualitative) shows that the ONB-MACF method outperforms existing state-of-the-art counterfactual generation methods across multiple quality metrics on diverse tabular datasets. This supports our hypothesis, showcasing the potential of data-morphology-based explainability strategies for trustworthy AI.<|reference_end|>
@article{pascual-triana2024overlap,
    title={Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A
  Data-Morphology-based Counterfactual Generation Method for Trustworthy
  Artificial Intelligence},
    author={Jos'e Daniel Pascual-Triana (1), Alberto Fern'andez (1), Javier Del
  Ser (1, 2 and 3), Francisco Herrera (1) ((1) Andalusian Institute of Data
  Science and Computational Intelligence (DASCI), University of Granada,
  Granada, Spain, (2) TECNALIA, Basque Research & Technology Alliance (BRTA),
  Derio, Spain, (3) University of the Basque Country (UPV/EHU), Bilbao, Spain)},
    journal={arXiv preprint arXiv:2405.12326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12326},
    primaryClass={cs.LG}
}