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Concrete Problems in AI Safety, Revisited

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arxiv 2401.10899 v1 pith:YT3F6NDZ submitted 2023-12-18 cs.CY cs.AI

classification cs.CYcs.AI
keywords safetydeploymentrealsystemsaccidentsalthoughanalysisarise
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models

    cs.LG 2025-07 conditional novelty 7.0 of 10

    A demographically diverse annotation dataset shows that safety perceptions for text-to-image outputs vary by rater identity and that conventional safety classifiers under-detect bias harms flagged by minority-group raters.

  2. Towards a Science of AI Agent Reliability

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Measuring 14 AI agents across two benchmarks, the paper finds 18 months of accuracy gains (≈0.21/yr) bought only small reliability gains (0.03–0.10/yr) under its consistency/robustness/predictability/safety framework.

  3. AI Safety for Everyone

    cs.CY 2025-02 conditional novelty 5.0 of 10

    A systematic review of 383 papers argues that AI safety research already covers a wide spectrum of concrete, near-term concerns and should be understood as part of traditional technological safety practice.

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

    cs.CY 2026-07 accept novelty 4.0 of 10

    AI safety is a systems-governance problem: six recurring organizational failure patterns from past disasters remain unlearned in AI development, so component-level fixes like benchmarks and alignment cannot deliver safety.

  5. Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects

    cs.CY 2025-05 conditional novelty 4.0 of 10

    A position paper argues that understanding AI's second-order effects requires moving from static benchmarks to an ecosystem of field testing, red teaming, and contextual evaluation.

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