# Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems

arXiv: https://arxiv.org/abs/2502.21250 · HTML: https://mlsanity.com/paper/2502.21250/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015)
- [Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains](https://mlsanity.com/paper/2006.08480/) (2020)
- [A Causal Analysis of Harm](https://mlsanity.com/paper/2210.05327/) (2022)
- [Practical Verification of Decision-Making in Agent-Based Autonomous Systems](https://mlsanity.com/paper/1310.2431/) (2013)
- [Quantifying Harm](https://mlsanity.com/paper/2209.15111/) (2022)
- [Machine Ethics and Automated Vehicles](https://mlsanity.com/paper/2010.15665/) (2020)
- [Understanding a Robot's Guiding Ethical Principles via Automatically Generated Explanations](https://mlsanity.com/paper/2206.10038/) (2022)

## Built on by (most cited first, 1 of 1)

- [Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Ethical Tutoring](https://mlsanity.com/paper/2603.27404/) (2026)

## Abstract (arXiv)

A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts, limiting their effectiveness across diverse scenarios. To address these challenges, we outline the necessary ingredients for building a holistic, meta-level framework that combines intermediate representations, probabilistic reasoning, and knowledge representation. The specifications therein emphasize scalability, supporting ethical reasoning at both individual decision-making levels and within the collective dynamics of multi-agent systems. By integrating theoretical principles with contextual factors, it facilitates structured and context-aware decision-making, ensuring alignment with overarching ethical standards. We further explore proposed theorems outlining how ethical reasoners should operate, offering a foundation for practical implementation. These constructs aim to support the development of robust and ethically reliable AI systems capable of navigating the complexities of real-world moral decision-making scenarios.