# AI Safety for Everyone

arXiv: https://arxiv.org/abs/2502.09288 · HTML: https://mlsanity.com/paper/2502.09288/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 14 of 14)

- [Murphys Laws of AI Alignment: Why the Gap Always Wins](https://mlsanity.com/paper/2509.05381/) (2025)
- [Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations](https://mlsanity.com/paper/2510.17256/) (2025)
- [Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models](https://mlsanity.com/paper/2601.22737/) (2026)
- [Risks of AI-driven product development and strategies for their mitigation](https://mlsanity.com/paper/2506.00047/) (2025)
- [Policy-Grounded Safety Evaluation of 20 Large Language Models](https://mlsanity.com/paper/2507.14719/) (2025)
- [Designing to Forget: Deep Semi-parametric Models for Unlearning](https://mlsanity.com/paper/2603.22870/) (2026)
- [Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization](https://mlsanity.com/paper/2604.05042/) (2026)
- [Agentic-imodels: Evolving agentic interpretability tools via autoresearch](https://mlsanity.com/paper/2605.03808/) (2026)
- [Structural Decoupling: A Scaffold-Flow Theory of Generalization and Alignment](https://mlsanity.com/paper/2506.20699/) (2025)
- [Governable AI: Provable Safety Under Extreme Threat Models](https://mlsanity.com/paper/2508.20411/) (2025)
- [RAPTOR+: A Visually Grounded Vision-Language Framework to Improve Clinical Trust and Auditability in Automated Cancer Referral Processing](https://mlsanity.com/paper/2605.25956/) (2026)
- [Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)](https://mlsanity.com/paper/2607.18263/) (2026)
- [The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems](https://mlsanity.com/paper/2607.19292/) (2026)
- [Safe-by-Design Learning via Energy-based Neural Networks](https://mlsanity.com/paper/2609.36942/) (2026)

## Abstract (arXiv)

Recent discussions and research in AI safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work on AI safety necessarily entails serious consideration of potential existential threats. However, this framing has three potential drawbacks: it may exclude researchers and practitioners who are committed to AI safety but approach the field from different angles; it could lead the public to mistakenly view AI safety as focused solely on existential scenarios rather than addressing a wide spectrum of safety challenges; and it risks creating resistance to safety measures among those who disagree with predictions of existential AI risks. Through a systematic literature review of primarily peer-reviewed research, we find a vast array of concrete safety work that addresses immediate and practical concerns with current AI systems. This includes crucial areas like adversarial robustness and interpretability, highlighting how AI safety research naturally extends existing technological and systems safety concerns and practices. Our findings suggest the need for an epistemically inclusive and pluralistic conception of AI safety that can accommodate the full range of safety considerations, motivations, and perspectives that currently shape the field.