Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

• URL: http://arxiv.org/abs/2607.14353v1
• Date: Wed, 15 Jul 2026 20:36:10 GMT
• Title: Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
• Abstract summary: Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems.<n>A common misconception is that human-made catastrophes are freak accidents, resulting from the inherently unforeseeable interactions in complex systems.<n>We outline several areas where the development and use of AI can benefit from learning these unlearned lessons.
• Score: 3.5391463753982886
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A common misconception is that these kinds of events are freak accidents, resulting from the inherently unforeseeable interactions in complex systems. Closer examination reveals that the risks and hazards were well-known beforehand but not acted upon due to social structural, political and economic factors. We outline several areas where the development and use of AI can benefit from learning these unlearned lessons: improved risk perception, communication, and analysis at the organizational level; traceability of requirements and responsibilities; and holistic approaches to responsibility and safety that include social and organizational dynamics as first-order engineering concerns. For each area, we offer concrete unlearned lessons and exemplify how they led to failure in prior accidents as well as examples of how these lessons remain unlearned for modern computing systems, particularly AI.

Related papers

• Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses [168.50301366360344]
  Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in safety-critical environments.<n>Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions.<n>This survey provides a comprehensive review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline.
  arXiv  Detail & Related papers  (2026-03-28T13:21:44Z)
• AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
  This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
  arXiv  Detail & Related papers  (2025-11-27T16:56:04Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems [2.226040060318401]
  We translate System Theoretic Process Analysis (STPA) for analyzing AI operation and development processes. We focus on systems that rely on machine learning algorithms and conductedA on three case studies. We find that key concepts and steps of conducting anA readily apply, albeit with a few adaptations tailored for AI systems.
  arXiv  Detail & Related papers  (2024-10-29T20:43:18Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• An Overview of Catastrophic AI Risks [38.84933208563934]
  This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories. Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs. organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents. rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
  arXiv  Detail & Related papers  (2023-06-21T03:35:06Z)
• A taxonomic system for failure cause analysis of open source AI incidents [6.85316573653194]
  This work demonstrates how to apply expert knowledge on the population of incidents in the AI Incident Database (AIID) to infer potential and likely technical causative factors that contribute to reported failures and harms. We present early work on a taxonomic system that covers a cascade of interrelated incident factors, from system goals (nearly always known) to methods / technologies (knowable in many cases) and technical failure causes (subject to expert analysis) of the implicated systems.
  arXiv  Detail & Related papers  (2022-11-14T11:21:30Z)
• Liability regimes in the age of AI: a use-case driven analysis of the burden of proof [1.7510020208193926]
  New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. But there is growing concerns about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties.
  arXiv  Detail & Related papers  (2022-11-03T13:55:36Z)
• X-Risk Analysis for AI Research [24.78742908726579]
  We provide a guide for how to analyze AI x-risk. First, we review how systems can be made safer today. Next, we discuss strategies for having long-term impacts on the safety of future systems.
  arXiv  Detail & Related papers  (2022-06-13T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.