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.

中文翻译：

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人工智能系统验证方法的系统文献综述

语境：

人工智能 (AI) 已进入日常活动，特别是通过机器学习 (ML) 等新技术。这些技术只需很少的领域知识即可实现。再加上用传统方法测试人工智能系统的难度，使得系统可信度成为一个紧迫的问题。

客观的：

本文研究了用于验证文献中报道的实际 AI 系统的方法。我们的目标是对现实环境中使用的方法进行分类和描述，以确保 AI 系统的可靠性。

方法：

系统的文献综述产生了 90 篇论文。论文中介绍的系统根据其领域、任务、复杂性和应用的验证方法进行了分析。

结果：

验证方法被综合成一个分类法，包括试验、模拟、以模型为中心的验证和专家意见。故障监视器、安全通道、冗余、投票以及输入和输出限制是用于在部署后持续验证系统的方法。

结论：

我们的结果阐明了应用于验证的现有策略。它们构成了研究中人工智能系统验证的综合、评估和改进的基础，以及在实践中验证单个系统的指南。虽然各种验证策略都得到了相对广泛的应用，但只有少数研究报告了持续验证。