mlsanityData to 14 Oct 2026

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

arXiv 2607.02197 ↗First posted 2 Jul 2026cs.CY cs.AI cs.CRcited by 4

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Abstract (arXiv)

The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this imperative, this paper presents an overview of AI risk assessment (identification and analysis) and management methodologies. It begins by reviewing the worldwide regulatory landscape that drives the need for systematic AI risk assessment. Then we characterize the spectrum of AI-related risks identified in the literature, from technical failures to ethical and social impacts. Subsequently, it reviews key risk assessment methodologies proposed for AI systems, focusing on general frameworks. The paper highlights best practices and illuminates methodological gaps, highlighting areas for further research on AI risk assessment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 2 Jul 2026.

Builds on

18 of 43 references.

References · 18

  1. 2023An Overview of Catastrophic AI Riskscited by 222
  2. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 61
  3. 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitmentcited by 33
  4. 2023TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AIcited by 26
  5. 2023Risk assessment at AGI companies: A review of popular risk assessment techniques from other safety-critical industriescited by 19
  6. 2025Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitmentcited by 13
  7. 2024A Taxonomy of Systemic Risks from General-Purpose AIcited by 12
  8. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 11
  9. 2024Biometrics and Behavior Analysis for Detecting Distractions in e-Learningcited by 10
  10. 2023Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Studycited by 9
  11. 2025AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resourcescited by 8
  12. 2025Privacy-Aware Detection of Fake Identity Documents: Methodology, Benchmark, and Improved Algorithms (FakeIDet2)cited by 7
6 more
  1. 2026Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2cited by 7
  2. 2024Personalized Weight Loss Management through Wearable Devices and Artificial Intelligencecited by 6
  3. 2026Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setupcited by 5
  4. 2026EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanationscited by 4
  5. 2021Achieving a Data-driven Risk Assessment Methodology for Ethical AIcited by 3
  6. 2025Between Innovation and Oversight: A Cross-Regional Study of AI Risk Management Frameworks in the EU, U.S., UK, and Chinacited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026CrimeNER Demo: Named-Entity Recognition in the Crime Domaincited by 0
  2. 2026AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanationscited by 0
  3. 2026VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Runningcited by 0
  4. 2026inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMscited by 0