Concrete Problems in AI Safety, Revisited

TL;DR

The paper questions whether existing AI safety taxonomies adequately capture the realities of safety failures in deployed systems and argues for a broader socio-technical framing. Using real-world case studies, it re-assesses the Amodei et al. taxonomy and examines Safe Exploration, Avoiding Negative Side Effects, and Scalable Oversight, highlighting gaps in current frameworks. The findings show that many safety failures stem from engineering-practice gaps, production-level harms, and insufficient stakeholder involvement, not merely algorithmic flaws. The work advocates inductive, case-based validation and governance that acknowledges power dynamics and deployment contexts to improve safety in real-world AI deployments.

Abstract

As AI systems proliferate in society, the AI community is increasingly preoccupied with the concept of AI Safety, namely the prevention of failures due to accidents that arise from an unanticipated departure of a system's behavior from designer intent in AI deployment. We demonstrate through an analysis of real world cases of such incidents that although current vocabulary captures a range of the encountered issues of AI deployment, an expanded socio-technical framing will be required for a more complete understanding of how AI systems and implemented safety mechanisms fail and succeed in real life.