  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.
