# Systematic Literature Review of Validation Methods for AI Systems

arXiv: https://arxiv.org/abs/2107.12190 · HTML: https://mlsanity.com/paper/2107.12190/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 10 of 10)

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- [Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems](https://mlsanity.com/paper/2411.14374/) (2024)
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- [Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems](https://mlsanity.com/paper/2609.25945/) (2026)
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## Abstract (arXiv)

Context: Artificial intelligence (AI) has made its way into everyday activities, particularly through new techniques such as machine learning (ML). These techniques are implementable with little domain knowledge. This, combined with the difficulty of testing AI systems with traditional methods, has made system trustworthiness a pressing issue. Objective: This paper studies the methods used to validate practical AI systems reported in the literature. Our goal is to classify and describe the methods that are used in realistic settings to ensure the dependability of AI systems. Method: A systematic literature review resulted in 90 papers. Systems presented in the papers were analysed based on their domain, task, complexity, and applied validation methods. Results: The validation methods were synthesized into a taxonomy consisting of trial, simulation, model-centred validation, and expert opinion. Failure monitors, safety channels, redundancy, voting, and input and output restrictions are methods used to continuously validate the systems after deployment. Conclusions: Our results clarify existing strategies applied to validation. They form a basis for the synthesization, assessment, and refinement of AI system validation in research and guidelines for validating individual systems in practice. While various validation strategies have all been relatively widely applied, only few studies report on continuous validation. Keywords: artificial intelligence, machine learning, validation, testing, V&V, systematic literature review.