mlsanityData to 7 Oct 2026

When to Trust AI: Advances and Challenges for Certification of Neural Networks

arXiv 2309.11196 ↗First posted 20 Sep 2023cs.LG cs.AI cs.CRcited by 6

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Abstract (arXiv)

Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 63 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

37 of 63 references.

References · 37

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25 more
  1. 2018Formal Security Analysis of Neural Networks using Symbolic Intervalscited by 185
  2. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 183
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 2.

6 papers.

  1. 2024Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AIcited by 3
  2. 2024Query languages for neural networkscited by 2
  3. 2026Spatiotemporal Robustness of Temporal Logic Tasks using Multi-Objective Reasoningcited by 1
  4. 2023Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurationscited by 0
  5. 2025VeRecycle: Reclaiming Guarantees from Probabilistic Certificates for Stochastic Dynamical Systems after Changecited by 0
  6. 2026A Model-Driven Approach for Developing Families of Reinforcement Learning Environmentscited by 0