基于多目标学习的可解释且公平的广义加性神经网络
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
浏览论文内容
中文总结 AI 辅助
本文提出多目标神经基模型（MONBM）框架，通过多目标进化学习同时优化准确性、可解释性和公平性，并引入定量可解释性指标及部分重训练策略，揭示可信度目标间的权衡关系。
中文摘要 AI 辅助
可解释性和公平性是可信人工智能（AI）中最受重视的两个维度。各种可解释的人工智能方法已被引入以提高可解释性。本文聚焦于基于神经网络（NN）的广义加性模型（GAMs），这是一类自解释模型。虽然现有大多数研究优先考虑提高基于神经网络的GAMs的准确性，但其可解释性在很大程度上仍未得到充分探索。为弥补这一空白，本文引入了评估基于神经网络的GAMs可解释性的显式定量指标，实证检验了其有效性，并探索了在这些模型中提高可解释性的策略。此外，同时显式优化可解释性和公平性，以及它们之间的权衡及其根本原因，仍未得到充分研究。为解决这一问题，我们提出了一种基于多目标进化学习的多目标神经基模型（MONBM）框架，以同时考虑准确性、可解释性和公平性。进一步开发了一种部分重训练策略，以促进进化多目标优化在深度模型架构中的实际应用。基于MONBM，本文揭示了这些维度之间的复杂关系及其背后的原因。该分析展示了如何将多目标优化与自解释模型相结合，以揭示可信度目标之间的关系。此外，MONBM获得了一组在不同维度间具有不同权衡的模型，并通过与最先进方法的比较验证了该方法的竞争力。
英文摘要
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve interpretability. This paper focuses on neural network (NN)-based generalized additive models (GAMs), a class of self-interpretable models. While most existing research has prioritized improving the accuracy of NN-based GAMs, their interpretability remains largely underexplored. To address this gap, this paper introduces explicit quantitative metrics for evaluating the interpretability of NN-based GAMs, empirically examines their effectiveness, and explores strategies for improving interpretability within these models. In addition, the simultaneous and explicit optimization of both interpretability and fairness, along with their trade-offs and the underlying reasons, remains underexplored. To address this, we propose a multi-objective neural basis model (MONBM) framework based on multi-objective evolutionary learning to consider accuracy, interpretability, and fairness simultaneously. A partial retraining strategy is further developed to facilitate the practical application of evolutionary multi-objective optimization to deep model architectures. Based on MONBM, this paper reveals the complex relationships between these dimensions and the reasons behind these intricate relationships. This analysis demonstrates how multi-objective optimization can be combined with self-interpretable models to reveal relationships among trustworthiness objectives. In addition, MONBM obtains a set of models with different trade-offs between dimensions, and the competitiveness of the approach is validated by comparing it with state-of-the-art methods.
发表机构
- Southern University of Science and Technology（南方科技大学）
- Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
- Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
- Nanyang Technological University（南洋理工大学）
- Lingnan University（岭南大学）
机构由 AI 辅助整理，请以论文原文为准。
