AI Quality - Guided Assessment

A Systematic approach to assure AI quality and compliance

A Systematic approach to assure AI quality and compliance

A Systematic approach to assure AI quality and compliance TO SCALE AI

  

Though ¾ of executives believe they risk going out of business in 5 years if they don’t scale AI, most of implemented AI projects currently fail to deliver their promised outcomes. To scale AI and utilise its full potential the associated risks must be managed explicitly.

 

We at TÜV SÜD have shifted our focus early on the quality of AI and have built comprehensive expertise in this field. Hence, we are now able to help organisations using or developing AI manage the risk of technical failures, organizational shortcomings, violation of regulatory or legal requirements, or reputational damage due to unintended ethical biases. 

 

Our guided assessment is based on standards, regulations and industry best practices and enables organisations to implement a systematic AI quality framework.

 

SCOPE AND SEQUENCE OF THE GUIDED ASSESSMENT

Our AI quality experts conduct this guided assessment starting with scoping of the actual AI component, solution or system. Like the AI Quality Readiness Analysis, but in significantly more depth, the assessment covers five key quality pillars that are examined:  

  • safety
  • security
  • legal
  • ethics 
  • performance

Using a risk-based approach, our quality framework is applied to the respective scope generating a company-specific quality profile. The profile is then being assessed to provide a clear understanding of the gaps and areas of improvement.

 

The organisation is now in the position to implement systematically a quality framework composed of respective standards and best practices meeting company values and regulations.

 

WHY CHOOSE TÜV SÜD AI quality service

 

At TÜV SÜD, we leverage our testing, inspection and certification expertise combined with deep knowledge of Industry 4.0, AI, IoT and Cybersecurity. We set the basis for organisations to systematically plan and implement AI quality, which is essential for AI developers and users to utilise the full potential of this transformative technology.

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