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Certification of processes for AI

In response to the growing number of artificial intelligence solutions, LNE has created a certification that provides users with objective criteria for making their selection, and enables developers to demonstrate that they have mastered all the stages of the AI life cycle and meet the performance, regulatory, confidentiality and ethical requirements of their customers.

Responding to issues of trust

Artificial intelligence (AI) has developed strongly in recent years in many professional sectors (collaborative industrial robots, inspection and maintenance robots, autonomous mobility systems, etc.) and in the home (personal assistance robots, medical devices, personal assistants, etc.), making it necessary to be able to reliably demonstrate the levels of performance, robustness, ethics and explicability achieved by the different AI solutions.

Thanks to our dual expertise in certification and evaluation of AI algorithms, LNE has decided to create a voluntary certification in this field that meets the needs of market players.

Market players Requirements AI certification responses
End users and buyers of AI solutions
  • Having confidence in the performance of the systems they acquire
  • The ability to choose from a multitude of existing solutions
  • A common, objective and clear reference to make their choice
  • Guarantees allowing to increase the acceptability of these technologies
AI developers, suppliers, integrators
  • Stand out from competitors
  • Guarantee the performance of their algorithms or systems
  • Demonstrate that they master all the stages of the AI life cycle and meet the requirements defined by their clients in terms of performance, confidentiality, ethics, as well as regulatory requirements
  • Use as a benchmark to guide their R&D and control efforts
Certification, the outcome of a consens

To build the certification standard and meet the need for trusted solutions, LNE has set up (in 2020) a working group representative of the AI community, bringing together developers, evaluators and end users of algorithms. It also collected opinions through a public call for comments.

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A certification of processes

This standard aims to define common requirements for the design, development, evaluation and maintenance processes of all types of AI functionalities using machine learning.

It therefore covers all business sectors in which AI is used, in order to ensure the application of best practices that promote confidence in AI. It is not intended to define requirements for AI functionalities and therefore specific to their uses.

The design, development, evaluation and maintenance processes covered by the standard are defined as follows:

 

Certified procedures Definition Examples of audited procedures
Design

Transforming an expression of requirements into functional specifications

 

Specification and inclusion of normative and regulatory requirements
Development Translate these specifications into a version of the AI functionality ready for evaluation

Learning

Quality of Databases

Evaluation Verify the conformity of the system to the specifications defined before its deployment Definition of evaluation protocols, metrics, in every evaluation tools to allow reporting on the effectiveness of these intelligent systems
Maintaining operational conditions Ensure compliance of the AI functionality with the defined specifications after its deployment and throughout its operating phase

All features necessary to maintain operating conditions

AI systems can evolve throughout their life with performance degradations

 

Why opt for certification?

Developers, suppliers and integrators of AI solutions, thanks to certification, will benefit from multiple advantages allowing them to bring an optimum guarantee of confidence to their customers. 

  • Demonstrate that they master all the stages of the AI life cycle and meet customer requirements for performance, regulatory compliance, confidentiality and ethics
  • Attest to the quality of development of their products containing AI
  • Establish a high level of confidence in their solutions by mastering the processes and best practices of the AI domain
  • Demonstrate that they have an organisation, structure and processes in place to produce quality and trusted AI over the long term
  • Have a competitive advantage
  • Benefit from optimal credibility by being audited by an independent and competent body, thus ensuring a good level of confidence in the respect of the requirements

Obtaining the AI certification

To obtain AI certification, developers, suppliers and integrators of AI solutions must meet the requirements of the "Certification standard of processes for AI: design, development, evaluation and maintenance in operational conditions".

The standard

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The certification standard of processes for artificial intelligence defines:

  • The certification procedure, including the scope of application
  • The requirements to be met by the applicant/owner
  • Admission, supervision and staffing arrangements

Procurement of the certification is governed by § 2 of the standard.

Download the AI certification standard

 

The steps to AI certification

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Steps to AI certification (english)
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Audit cycle

AI certification audit cycle

After obtaining your certification, you enter in a cycle of annual audits:

  • An initial follow-up audit to be carried out at 12 months
  • A second follow-up audit to be carried out at 24 months,
  • A renewal audit after 3 years

Applying for certification

For any request with LNE Digital certification, artificial intelligence, contact us by clicking on "Obtain a quote".

LNE's strong points

  • Over 10 years of experience in evaluating intelligent technologies
  • More than 900 evaluations of artificial intelligence systems, including language processing, image processing and robotics
  • Expert in the constitution and use of large, quality, structured and annotated test databases
  • Participation in the AFNOR commission on artificial intelligence, in the AFNOR strategic committee on information and digital communication and in section 81 of the UNM on industrial robotics
  • Participation in the development of key resources for AI assessment and development

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