# Towards Verified Artificial Intelligence

arXiv: https://arxiv.org/abs/1606.08514 · HTML: https://mlsanity.com/paper/1606.08514/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2016.

## Built on by (most cited first, 30 of 36)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems](https://mlsanity.com/paper/2405.06624/) (2024)
- [A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://mlsanity.com/paper/2305.11391/) (2023)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Logic-Based Explainability in Machine Learning](https://mlsanity.com/paper/2211.00541/) (2022)
- [Categorical Foundations of Gradient-Based Learning](https://mlsanity.com/paper/2103.01931/) (2021)
- [Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack](https://mlsanity.com/paper/2308.11894/) (2023)
- [A System-Level View on Out-of-Distribution Data in Robotics](https://mlsanity.com/paper/2212.14020/) (2022)
- [Taxonomy of Machine Learning Safety: A Survey and Primer](https://mlsanity.com/paper/2106.04823/) (2021)
- [From Robustness to Explainability and Back Again](https://mlsanity.com/paper/2306.03048/) (2023)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [Disproving XAI Myths with Formal Methods -- Initial Results](https://mlsanity.com/paper/2306.01744/) (2023)
- [If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components](https://mlsanity.com/paper/2202.03930/) (2022)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Design Principles for Lifelong Learning AI Accelerators](https://mlsanity.com/paper/2310.04467/) (2023)
- [Parallel and Multi-Objective Falsification with Scenic and VerifAI](https://mlsanity.com/paper/2107.04164/) (2021)
- [PAC Verification of Statistical Algorithms](https://mlsanity.com/paper/2211.17096/) (2022)
- [Customizable Reference Runtime Monitoring of Neural Networks using Resolution Boxes](https://mlsanity.com/paper/2104.14435/) (2021)
- [Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification](https://mlsanity.com/paper/2205.10906/) (2022)
- [What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems](https://mlsanity.com/paper/2307.11784/) (2023)
- [Weighted Graph-Based Signal Temporal Logic Inference Using Neural Networks](https://mlsanity.com/paper/2109.08078/) (2021)
- [Space Explanations of Neural Network Classification](https://mlsanity.com/paper/2511.22498/) (2025)
- [Skilldex: A Package Manager and Registry for Agent Skill Packages with Hierarchical Scope-Based Distribution](https://mlsanity.com/paper/2604.16911/) (2026)
- [Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes](https://mlsanity.com/paper/2312.16364/) (2023)
- [Deep Learning with Parametric Lenses](https://mlsanity.com/paper/2404.00408/) (2024)
- [Test and Evaluation of Quadrupedal Walking Gaits through Sim2Real Gap Quantification](https://mlsanity.com/paper/2201.01323/) (2022)
- [Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems](https://mlsanity.com/paper/2204.12402/) (2022)
- [Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches](https://mlsanity.com/paper/2305.17941/) (2023)
- [Learning Contextual Runtime Monitors for Safe AI-Based Autonomy](https://mlsanity.com/paper/2601.20666/) (2026)
- [Algorithmic Unverifiability of Safety for Fixed and Recursively Self-Improving Systems](https://mlsanity.com/paper/2606.28639/) (2026)

## Abstract (arXiv)

Verified artificial intelligence (AI) is the goal of designing AI-based systems that that have strong, ideally provable, assurances of correctness with respect to mathematically-specified requirements. This paper considers Verified AI from a formal methods perspective. We describe five challenges for achieving Verified AI, and five corresponding principles for addressing these challenges.