The AI Alignment Paradox
Not on the map. Why?
Abstract (arXiv)
The field of AI alignment aims to steer AI systems toward human goals, preferences, and ethical principles. Its contributions have been instrumental for improving the output quality, safety, and trustworthiness of today's AI models. This perspective article draws attention to a fundamental challenge we see in all AI alignment endeavors, which we term the "AI alignment paradox": The better we align AI models with our values, the easier we may make it for adversaries to misalign the models. We illustrate the paradox by sketching three concrete example incarnations for the case of language models, each corresponding to a distinct way in which adversaries might exploit the paradox. With AI's increasing real-world impact, it is imperative that a broad community of researchers be aware of the AI alignment paradox and work to find ways to mitigate it, in order to ensure the beneficial use of AI for the good of humanity.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 31 May 2024.
Builds on
8 of 10 references.
References · 8
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,154
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
- 2024A Roadmap to Pluralistic Alignmentcited by 184
- 2023Fundamental Limitations of Alignment in Large Language Modelscited by 155
- 2024JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMscited by 95
- 2024Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Spacecited by 91
- 2020The Radicalization Risks of GPT-3 and Advanced Neural Language Modelscited by 76
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Citing papers per year: 2025: 1.
1 paper.
- 2025From Model Training to Model Raisingcited by 1
- 2025From Model Training to Model Raising12 Nov 2025
Similar, not cited
Close in wording, no citation either way
- 2026Toward a Theory of Value in AI Alignment
- 2024Position: Towards Bidirectional Human-AI Alignment
- 2023The Alignment Problem in Context
- 2023A Multi-Level Framework for the AI Alignment Problem
- 2025Normative Conflicts and Shallow AI Alignment
- 2024ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs
- 2024A Moral Imperative: The Need for Continual Superalignment of Large Language Models
- 2024Beyond Preferences in AI Alignment