标题： 伦理-BERT：一种用于伦理和非伦理内容分类的增强深度学习模型 显示英文标题

标题： Ethic-BERT: An Enhanced Deep Learning Model for Ethical and Non-Ethical Content Classification

摘要： 开发能够进行微妙伦理推理的AI系统至关重要，因为它们越来越多地影响人类决策，但现有模型通常依赖于表面相关性，而不是基于原则的道德理解。 本文介绍了Ethic-BERT，这是一个基于BERT的模型，用于四个领域的伦理内容分类：常识、正义、美德和义务论。 利用ETHICS数据集，我们的方法结合了强大的预处理以解决词汇稀疏性和上下文模糊性，同时采用了先进的微调策略，如完整模型解冻、梯度累积和自适应学习率调度。 为了评估鲁棒性，我们采用了一个对抗过滤的“Hard Test”分割，隔离复杂的伦理困境。 实验结果表明，Ethic-BERT优于基线模型，在标准测试中平均准确率达到82.32%，在正义和美德方面有显著提升。 此外，提出的Ethic-BERT在HardTest中平均准确率提高了15.28%。 这些发现有助于通过具有偏差意识的预处理和提出的增强AI模型实现性能提升和可靠决策。 显示英文摘要

摘要： Developing AI systems capable of nuanced ethical reasoning is critical as they increasingly influence human decisions, yet existing models often rely on superficial correlations rather than principled moral understanding. This paper introduces Ethic-BERT, a BERT-based model for ethical content classification across four domains: Commonsense, Justice, Virtue, and Deontology. Leveraging the ETHICS dataset, our approach integrates robust preprocessing to address vocabulary sparsity and contextual ambiguities, alongside advanced fine-tuning strategies like full model unfreezing, gradient accumulation, and adaptive learning rate scheduling. To evaluate robustness, we employ an adversarially filtered "Hard Test" split, isolating complex ethical dilemmas. Experimental results demonstrate Ethic-BERT's superiority over baseline models, achieving 82.32% average accuracy on the standard test, with notable improvements in Justice and Virtue. In addition, the proposed Ethic-BERT attains 15.28% average accuracy improvement in the HardTest. These findings contribute to performance improvement and reliable decision-making using bias-aware preprocessing and proposed enhanced AI model.