# Can Artificial Intelligence Embody Moral Values?

arXiv: https://arxiv.org/abs/2408.12250 · HTML: https://mlsanity.com/paper/2408.12250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Foundational Challenges in Assuring Alignment and Safety of Large Language Models](https://mlsanity.com/paper/2404.09932/) (2024)
- [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [What Would Jiminy Cricket Do? Towards Agents That Behave Morally](https://mlsanity.com/paper/2110.13136/) (2021)
- [Discovering Agents](https://mlsanity.com/paper/2208.08345/) (2022)
- [A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents](https://mlsanity.com/paper/1712.04172/) (2017)
- [Implementations in Machine Ethics: A Survey](https://mlsanity.com/paper/2001.07573/) (2020)
- [Algorithms are not neutral: Bias in collaborative filtering](https://mlsanity.com/paper/2105.01031/) (2021)
- [CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation](https://mlsanity.com/paper/2308.05978/) (2023)

## Abstract (arXiv)

The neutrality thesis holds that technology cannot be laden with values. This long-standing view has faced critiques, but much of the argumentation against neutrality has focused on traditional, non-smart technologies like bridges and razors. In contrast, AI is a smart technology increasingly used in high-stakes domains like healthcare, finance, and policing, where its decisions can cause moral harm. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm. We provide a conceptual framework discussing the neutrality thesis, values, and AI. Moreover, we examine two approaches to designing computational models of morality, artificial conscience and ethical prompting, and present empirical evidence from text-based game environments that artificial agents with such models exhibit more ethical behavior compared to agents without these models. The findings support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.