![[2602.13268] Expected Moral Shortfall for Ethical Competence in Decision-making Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13268] Expected Moral Shortfall for Ethical Competence in Decision-making Models
Summary
This paper explores the integration of moral cognition into AI decision-making models, introducing the concept of Expected Moral Shortfall (EMS) to enhance ethical competence.
Why It Matters
As AI systems increasingly influence societal decisions, ensuring ethical alignment in their operations is crucial. This research provides a framework for assessing and improving the ethical competence of AI models, addressing a significant gap in AI ethics.
Key Takeaways
- Introduces Expected Moral Shortfall (EMS) as a metric for ethical decision-making in AI.
- Compares various techniques for instilling ethical competence in AI models.
- Demonstrates a novel mathematical approach to quantify morality in AI applications.
- Discusses the trade-offs between AI performance metrics and ethical considerations.
- Highlights the importance of ethical alignment in AI's social impact.
Computer Science > Computers and Society arXiv:2602.13268 (cs) [Submitted on 4 Feb 2026] Title:Expected Moral Shortfall for Ethical Competence in Decision-making Models Authors:Aisha Aijaz, Raghava Mutharaju, Manohar Kumar View a PDF of the paper titled Expected Moral Shortfall for Ethical Competence in Decision-making Models, by Aisha Aijaz and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Computers and Society (cs.CY)...
