提出道德风险指标EMS，让AI模型在决策中更符合伦理。
Expected Moral Shortfall for Ethical Competence in Decision-making Models
- 用预期道德短板（EMS）量化道德风险，引导AI优化伦理表现。
- 在两个数据集上验证，相比传统方法伦理表现提升明显。
- 适合关注AI伦理对齐与社会影响的研究者和开发者。
道德认知是人工智能决策中的关键但未被充分探索的方面。无论应用领域如何，都应考虑实现伦理对齐的决策。本文提出三方面贡献：首先，对比分析了将伦理能力注入AI模型的技术，并在多个性能指标下进行评估；其次，提出一种道德的数学离散化方法，并在两个数据集上测试其实际应用效果；该方法将道德价值建模为最不道德情形下的损失风险，即预期道德短板（EMS），并引导AI模型最小化这一指标以同时提升性能与伦理能力；最后，讨论了模型性能、复杂度与伦理能力规模之间的权衡，以揭示实际社会影响的真正范围。
原文摘要 · Abstract (English)
Moral cognition is a crucial yet underexplored aspect of decision-making in AI models. Regardless of the application domain, it should be a consideration that allows for ethically aligned decision-making. This paper presents a multifaceted contribution to this research space. Firstly, a comparative analysis of techniques to instill ethical competence into AI models has been presented to gauge them on multiple performance metrics. Second, a novel mathematical discretization of morality and a demonstration of its real-life application have been conveyed and tested against other techniques on two datasets. This value is modeled as the risk of loss incurred by the least moral cases, or an Expected Moral Shortfall (EMS), which we direct the AI model to minimize in order to maximize its performance while retaining ethical competence. Lastly, the paper discusses the tradeoff between preliminary AI decision-making metrics such as model performance, complexity, and scale of ethical competence to recognize the true extent of practical social impact.
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