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Artificial intelligence risk management framework :
16 references·6 citing·51 second hop
Artificial intelligence risk management framework : (2024)Artificial intelligence risk management f…Red-Teaming for Generative AI: Silver Bullet or Security Theater? (2024)Red-Teaming for Generative AI…Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models (2024)Large Legal Fictions: Profili…Deepfakes, Phrenology, Surveillance, and More! 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Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents (2025)Who is Responsible When AI Fa…Two types of AI existential risk: decisive and accumulative (2025)Two types of AI existential r…Limitations of risk-based artificial intelligence regulation: a structuration theory approach (2025)Limitations of risk-based art…Opportunities of Gen AI in the Banking Industry with regards to the AI Act, GDPR, Data Act and DORA (2024)Opportunities of Gen AI in th…Ethical considerations in Risk management of autonomous and intelligent systems (2024)Ethical considerations in Ris…A NEW MEASURE OF RANK CORRELATION (1938)Individual Choice Behavior: A Theoretical Analysis. (1960)Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market (2006)On the Opportunities and Risks of Foundation Models (2021)Can Large Language Models Transform Computational Social Science? 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Abolitionist tools for the new Jim Code (2020)Regulating ChatGPT and other Large Generative AI Models (2023)Visibility into AI Agents (2024)Why ‘open’ AI systems are actually closed, and why this matters (2024)Scoping studies: towards a methodological framework (2005)Using thematic analysis in psychology (2006)PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation (2018)Auditing large language models: a three-layered approach (2023)Harms from Increasingly Agentic Algorithmic Systems (2023)Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations (2024)Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics (2023)Large Language Models and the Reverse Turing Test (2023)How to optimize the systematic review process using AI tools (2024)ChatGPT is incredible (at being average) (2025)Using Artificial Intelligence Tools as Second Reviewers for Data Extraction in Systematic Reviews: A Performance Comparison of Two AI Tools Against Human Reviewers (2025)Influence tactics, affect, and exchange quality in supervisor-subordinate interactions: A laboratory experiment and field study. 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Faltering Innovation Confronts the Six Headwinds (2012)Constitutional AI: Harmlessness from AI Feedback (2022)Deception abilities emerged in large language models (2024)The benefits and dangers of anthropomorphic conversational agents (2025)A Taxonomy of Privacy (2006)The global landscape of AI ethics guidelines (2019)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)The Convergence of Artificial Intelligence and Privacy: Navigating Innovation with Ethical Considerations (2024)A cognitive approach to human–AI complementarity in dynamic decision-making (2025)Exploring Parent-Child Perceptions on Safety in Generative AI: Concerns, Mitigation Strategies, and Design Implications (2025)A Coefficient of Agreement for Nominal Scales (1960)Judgment under Uncertainty: Heuristics and Biases (1974)The Measurement of Observer Agreement for Categorical Data (1977)Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity (2024)Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools (2025)Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges (2025)Practical and ethical challenges of large language models in education: A systematic scoping review (2023)Talking about Large Language Models (2024)A Comprehensive Review on Generative AI for Education (2024)Generative AI enhances individual creativity but reduces the collective diversity of novel content (2024)Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation (2024)
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References

  • 2021Ethical and social risks of harm from Language Models1,225 cited
  • 2022Taxonomy of Risks posed by Language Models861 cited
  • 2023Art and the science of generative AI783 cited
  • 2024Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models551 cited
  • 2023Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction276 cited
  • 2024AI deception: A survey of examples, risks, and potential solutions243 cited
  • 2021Algorithmic monoculture and social welfare144 cited
  • 2023Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models94 cited
  • 2023The Gradient of Generative AI Release: Methods and Considerations90 cited
  • 2024Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks82 cited
  • 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?75 cited
  • 2023Model evaluation for extreme risks62 cited
  • 2022Picking on the Same Person: Does Algorithmic Monoculture Lead to Outcome Homogenization?57 cited
  • 2023Taxonomizing and Measuring Representational Harms: A Look at Image Tagging32 cited
  • 2023Why We Need to Know More: Exploring the State of AI Incident Documentation Practices

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24 cited
  • 2020Overcoming Failures of Imagination in AI Infused System Development and Deployment21 cited
  • Cited by

    • 2025Two types of AI existential risk: decisive and accumulative42 cited
    • 2024Ethical considerations in Risk management of autonomous and intelligent systems25 cited
    • 2024Opportunities of Gen AI in the Banking Industry with regards to the AI Act, GDPR, Data Act and DORA14 cited
    • 2025Limitations of risk-based artificial intelligence regulation: a structuration theory approach13 cited
    • 2025Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents10 cited
    • 2026From tool to collaborator: rethinking GIScience as GIS becomes agentic and autonomous4 cited