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Understand and Test
Artificial Intelligence

Artificial intelligence is no longer a topic for the future. Today, it is used productively in almost all industries - from automated decisions to generative systems.

However, with its increasing use comes a central challenge: how do you ensure that AI systems function reliably, fairly and in accordance with the rules?

This page provides a structured overview of modern AI, typical risks and the role of AI testing as a key success factor.

Comprehensive AI testing

We test AI systems along their entire life cycle - from data to models to application.

Recognizing risks

We identify weaknesses such as bias, misconduct and security risks in AI systems.

Creating transparency

We make decisions made by AI systems comprehensible and easy to understand.

Enabling trust

We support the safe, fair and compliant use of AI systems.


What risks does AI pose?

With the increasing use of AI systems, new risks arise that differ significantly from traditional software.

While traditional systems work deterministically, AI models make probabilistic decisions - with corresponding new challenges for quality, safety and control.

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AI testing - what is it?

AI testing refers to the systematic testing of AI systems over their entire lifespan.

In contrast to classic software testing, it is not just about functionality, but about the behavior of systems under uncertainty.

Typical questions are:

  • Does the system make reliable decisions?
  • Is the behavior stable and robust?
  • Are the results comprehensible and fair?
  • Does the system meet regulatory requirements?

The areas of safety, governance and fairness in particular are becoming increasingly important.

Symbol AI brain

How do you test AI systems?

Testing AI is fundamentally different from classic testing.
New methods are used instead of deterministic tests:

  • Scenario-based tests (real-world cases)
  • Adversarial testing (targeted troubleshooting)
  • Bias and fairness analyses
  • Prompt and input variations
  • Continuous monitoring after deployment

AI systems need to be checked not just once, but continuously.

Typical areas of application for AI

Today, AI is used in numerous business-critical areas.

These include, among others:

  • HR and recruiting
  • Lending and scoring
  • medical diagnostics
  • public administration
  • Customer service and chatbots
  • Fraud detection

Many of these applications are associated with increased risks and require structured verification procedures.

Symbolic application areas of AI
Symbolic testing of AI

Why traditional software testing is not enough

AI systems are fundamentally different from conventional software.

  • Results are not deterministic
  • Behavior changes with data and model updates
  • Systems react sensitively to inputs

This means that classic test methods are only suitable to a limited extent.

Modern approaches in AI testing include

  • Scenario-based testing
  • Adversarial testing
  • Continuous monitoring

Classification: AI testing in the overall context

The testing of AI combines

  • technical quality assurance
  • risk management
  • regulatory requirements
  • organizational aspects

This makes it a central building block for the safe use of AI.

Symbol Classification AI testing in the overall context

Do you need individual quality assurance for your AI?

Please contact us.

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Case Studies

Find out how we turn complex test projects into measurable successes. Our practical examples show how we work with our customers to ensure quality and minimize risks.

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