# Introduction to AI Safety, Ethics, and Society

arXiv: https://arxiv.org/abs/2411.01042 · HTML: https://mlsanity.com/paper/2411.01042/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2024.

## Built on by (most cited first, 15 of 15)

- [Virology Capabilities Test (VCT): A Multimodal Virology Q&A Benchmark](https://mlsanity.com/paper/2504.16137/) (2025)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [Zero-Shot Performance Prediction for Probabilistic Scaling Laws](https://mlsanity.com/paper/2510.16743/) (2025)
- [Truthfulness Despite Weak Supervision: Evaluating and Training LLMs Using Peer Prediction](https://mlsanity.com/paper/2601.20299/) (2026)
- [MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents](https://mlsanity.com/paper/2602.13372/) (2026)
- [A New Perspective On AI Safety Through Control Theory Methodologies](https://mlsanity.com/paper/2506.23703/) (2025)
- [Factual Inconsistency in Data-to-Text Generation Scales Exponentially with LLM Size: A Statistical Validation](https://mlsanity.com/paper/2502.12372/) (2025)
- [Threshold Crossings as Tail Events for Catastrophic AI Risk](https://mlsanity.com/paper/2503.18979/) (2025)
- [Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks](https://mlsanity.com/paper/2505.13565/) (2025)
- [DCV-ROOD Evaluation Framework: Dual Cross-Validation for Robust Out-of-Distribution Detection](https://mlsanity.com/paper/2509.05778/) (2025)
- [A Ratio-Based Shapley Value for Collaborative Machine Learning - Extended Version](https://mlsanity.com/paper/2510.13261/) (2025)
- [Perspectives on a Reliability Monitoring Framework for Agentic AI Systems](https://mlsanity.com/paper/2511.09178/) (2025)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [Active Budget Allocation for Efficient Scaling Law Estimation via Surrogate-Guided Pruning](https://mlsanity.com/paper/2605.17234/) (2026)
- [Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics](https://mlsanity.com/paper/2608.05656/) (2026)

## Abstract (arXiv)

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.