让神经网络满足全局关系约束，确保公平性与鲁棒性
SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
- 基于SMiLE框架扩展，支持覆盖全输入空间的全局约束
- 在合成与真实数据上实现高精度且完全满足约束条件
- 适合需严格合规的医疗、金融等高风险应用
人工智能系统在需要保证鲁棒性、公平性或领域特定属性的场景中日益重要，这些属性对监管合规和人类价值观对齐至关重要。然而，尤其在神经网络中，属性强制执行极具挑战性，现有方法通常仅适用于特定约束或局部属性（围绕数据点定义），或无法提供完整保障。本文通过扩展近期提出的神经网络属性强制框架SMiLE，使其支持全局关系属性（在整个输入空间上定义）。所提方法在模型复杂度下具有良好的可扩展性，兼容通用属性与主干网络，并提供完整的满足性保障。我们在单调性、全局鲁棒性和个体公平性任务上，针对回归与分类问题，在合成与真实数据上评估了SMiLE。结果表明，该方法在准确率和运行时间上与专用基线相当，且在通用性和保障级别上显著更优。总体而言，这些结果凸显了SMiLE框架作为未来研究与应用平台的潜力。
原文摘要 · Abstract (English)
Artificial Intelligence systems are increasingly deployed in settings where ensuring robustness, fairness, or domain-specific properties is essential for regulation compliance and alignment with human values. However, especially on Neural Networks, property enforcement is very challenging, and existing methods are limited to specific constraints or local properties (defined around datapoints), or fail to provide full guarantees. We tackle these limitations by extending SMiLE, a recently proposed enforcement framework for NNs, to support global relational properties (defined over the entire input space). The proposed approach scales well with model complexity, accommodates general properties and backbones, and provides full satisfaction guarantees. We evaluate SMiLE on monotonicity, global robustness, and individual fairness, on synthetic and real data, for regression and classification tasks. Our approach is competitive with property-specific baselines in terms of accuracy and runtime, and strictly superior in terms of generality and level of guarantees. Overall, our results emphasize the potential of the SMiLE framework as a platform for future research and applications.
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