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Can Artificial Intelligence Embody Moral Values?

arXiv 2408.12250 ↗First posted 22 Aug 2024cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 55 references that are in the map. First posted on arXiv 22 Aug 2024.

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References · 16

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  4. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  5. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  6. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  7. 2024Foundational Challenges in Assuring Alignment and Safety of Large Language Modelscited by 193
  8. 2023Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmarkcited by 174
  9. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 134
  10. 2022The Fallacy of AI Functionalitycited by 88
  11. 2021What Would Jiminy Cricket Do? Towards Agents That Behave Morallycited by 57
  12. 2022Discovering Agentscited by 28
4 more
  1. 2017A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agentscited by 18
  2. 2020Implementations in Machine Ethics: A Surveycited by 16
  3. 2021Algorithms are not neutral: Bias in collaborative filteringcited by 3
  4. 2023CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigationcited by 1

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