Introducing \delta-XAI: a novel sensitivity-based method for local AI explanations
<|reference_start|>Introducing \delta-XAI: a novel sensitivity-based method for local AI explanations: Explainable Artificial Intelligence (XAI) is central to the debate on integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into clinical practice. High-performing AI/ML models, such as ensemble learners and deep neural networks, often lack interpretability, hampering clinicians' trust in their predictions. To address this, XAI techniques are being developed to describe AI/ML predictions in human-understandable terms. One promising direction is the adaptation of sensitivity analysis (SA) and global sensitivity analysis (GSA), which inherently rank model inputs by their impact on predictions. Here, we introduce a novel delta-XAI method that provides local explanations of ML model predictions by extending the delta index, a GSA metric. The delta-XAI index assesses the impact of each feature's value on the predicted output for individual instances in both regression and classification problems. We formalize the delta-XAI index and provide code for its implementation. The delta-XAI method was evaluated on simulated scenarios using linear regression models, with Shapley values serving as a benchmark. Results showed that the delta-XAI index is generally consistent with Shapley values, with notable discrepancies in models with highly impactful or extreme feature values. The delta-XAI index demonstrated higher sensitivity in detecting dominant features and handling extreme feature values. Qualitatively, the delta-XAI provides intuitive explanations by leveraging probability density functions, making feature rankings clearer and more explainable for practitioners. Overall, the delta-XAI method appears promising for robustly obtaining local explanations of ML model predictions. Further investigations in real-world clinical settings will be conducted to evaluate its impact on AI-assisted clinical workflows.<|reference_end|>
@article{de carlo2024introducing,
    title={Introducing {\delta}-XAI: a novel sensitivity-based method for local AI
  explanations},
    author={Alessandro De Carlo, Enea Parimbelli, Nicola Melillo, Giovanna Nicora},
    journal={arXiv preprint arXiv:2407.18343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18343},
    primaryClass={cs.LG cs.AI}
}