# Disentangling AI Alignment: A Structured Taxonomy Beyond Safety and Ethics

arXiv: https://arxiv.org/abs/2506.06286 · HTML: https://mlsanity.com/paper/2506.06286/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 5 of 5)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models](https://mlsanity.com/paper/2308.09490/) (2023)
- [Evaluating the Impact of Personalized Value Alignment in Human-Robot Interaction: Insights into Trust and Team Performance Outcomes](https://mlsanity.com/paper/2311.16051/) (2023)

## Built on by (most cited first, 3 of 3)

- [Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment](https://mlsanity.com/paper/2601.10520/) (2026)
- [Justifications for Democratizing AI Alignment and Their Prospects](https://mlsanity.com/paper/2507.19548/) (2025)
- [Anticipatory Human Oversight of Agentic AI: A Philosophical Account](https://mlsanity.com/paper/2609.24242/) (2026)

## Abstract (arXiv)

Recent advances in AI research make it increasingly plausible that artificial agents with consequential real-world impact will soon operate beyond tightly controlled environments. Ensuring that these agents are not only safe but that they adhere to broader normative expectations is thus an urgent interdisciplinary challenge. Multiple fields -- notably AI Safety, AI Alignment, and Machine Ethics -- claim to contribute to this task. However, the conceptual boundaries and interrelations among these domains remain vague, leaving researchers without clear guidance in positioning their work. To address this meta-challenge, we develop a structured conceptual framework for understanding AI alignment. Rather than focusing solely on alignment goals, we introduce a taxonomy distinguishing the alignment aim (safety, ethicality, legality, etc.), scope (outcome vs. execution), and constituency (individual vs. collective). This structural approach reveals multiple legitimate alignment configurations, providing a foundation for practical and philosophical integration across domains, and clarifying what it might mean for an agent to be aligned all-things-considered.