Khái niệm cốt lõi
Machine learning models can be challenging to interpret, leading to the emergence of Counterfactual Explanations (CEs) in eXplainable Artificial Intelligence (XAI). The UFCE methodology addresses limitations in current CE algorithms and aims to provide actionable explanations based on user feedback.
Tóm tắt
Counterfactual explanations play a crucial role in understanding machine learning models. The UFCE methodology introduces user feedback-based counterfactual explanations, addressing practicality and feasibility concerns. By conducting experiments across various datasets, UFCE outperforms existing CE methods in terms of proximity, sparsity, and feasibility.
Machine learning models are widely used but often lack transparency. Counterfactual explanations offer insights into decision-making processes. UFCE enhances the generation of meaningful and actionable explanations by incorporating user constraints.
The study compares UFCE with two well-known CE methods, DiCE and AR, demonstrating superior performance. UFCE focuses on minimal modifications within a subset of features while considering feature dependence and user constraints.
Through rigorous evaluation metrics like sparsity, proximity, actionability, plausibility, and feasibility, UFCE proves to be a promising algorithm in the field of XAI. The open-source implementation of UFCE further supports future investigations.
Thống kê
Machine learning models are widely used in real-world applications.
Current CE algorithms operate within the entire feature space.
UFCE outperforms two well-known CE methods in terms of proximity, sparsity, and feasibility.